diff --git a/.github/workflows/run_tests.yml b/.github/workflows/run_tests.yml new file mode 100644 index 0000000..727a98f --- /dev/null +++ b/.github/workflows/run_tests.yml @@ -0,0 +1,60 @@ +name: test + +on: + push: + branches: + - "main" + - "master" + - "dev" + paths: + - "**.py" + - ".github/workflows/*test*.yml" + - "pyproject.toml" + - "poetry.lock" + - "requirements/requirements*.txt" + pull_request: + paths: + - "**.py" + - ".github/workflows/*test*.yml" + - "pyproject.toml" + - "poetry.lock" + - "requirements/requirements*.txt" + +jobs: + test: + name: "test Python ${{ matrix.python }} on ${{ matrix.os }}" + runs-on: "${{ matrix.os }}" + strategy: + fail-fast: false + matrix: + os: ["ubuntu-latest"] + python: ["3.9", "3.10", "3.11"] + include: + - os: "macos-latest" + python-version: "3.10" + defaults: + run: + shell: "bash -eo pipefail {0}" + + steps: + - uses: "actions/checkout@main" + - uses: "actions/setup-python@main" + with: + python-version: "${{ matrix.python }}" + - uses: "actions/cache@main" + id: "cache" + with: + path: "${{ env.pythonLocation }}" + key: "test-${{ runner.os }}-${{ env.pythonLocation }}-${{ hashFiles('pyproject.toml', 'requirements/*') }}" + - run: "python --version" + - run: "python -mpip install -U pip" + - run: "python -mpip --version" + - run: "python -mpip install -r requirements/requirements.txt" + - name: "Install requirements" + - run: "python -mpip install ./" + - run: "python -mpip cache info" + - run: "python -mpip freeze" + - name: "Run tests" + - run: "pytest tests/" + env: + PYTHONPATH: ${{ github.workspace }}/tests diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 7525b64..76a1e8d 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -80,18 +80,6 @@ repos: "-o", "requirements/requirements.txt", ] - - id: "poetry-export" - name: "poetry export dev" - args: - [ - "--with", - "dev", - "-f", - "requirements.txt", - "-o", - "requirements/requirements-dev.txt", - ] - - repo: "local" hooks: - id: "mypy" diff --git a/analysea/steps.py b/analysea/steps.py index 382e46b..e20d324 100644 --- a/analysea/steps.py +++ b/analysea/steps.py @@ -8,8 +8,6 @@ import numpy.typing as npt import pandas as pd import ruptures as rpt -from scipy import interpolate -from skimage.restoration import denoise_tv_chambolle from analysea.utils import detect_time_step @@ -69,18 +67,3 @@ def remove_steps_simple(df: pd.DataFrame, threshold: float) -> Tuple[pd.DataFram step = df.interpolate().iloc[stepx : steps_ix[i + 1]].mean() step_function.iloc[stepx : steps_ix[i + 1]] = step return step_function, steps_ix - - -def step_function_TV(df: pd.DataFrame, weight: float = 1) -> Tuple[pd.DataFrame, npt.NDArray[Any]]: - idx = range(0, len(df), 200) - signal = np.array(df.interpolate())[idx] - # adjust the parameters - signal_denoise = denoise_tv_chambolle(signal, weight=weight) - # x_step = -2*np.cumsum(signal_denoise) - # step_indicator = x_step == x_step.max() - f = interpolate.interp1d( - idx, signal_denoise, fill_value="extrapolate" - ) # for extrapolation in last scipy version - res = pd.Series(data=f(range(len(df))), index=df.index) - # - return remove_steps_simple(res, res.std()) diff --git a/analysea/tide.py b/analysea/tide.py index dac0fef..5de5f68 100644 --- a/analysea/tide.py +++ b/analysea/tide.py @@ -107,6 +107,26 @@ def calc_constituents( lat: float = 0.0, **kwargs: Dict[str, Any] | None, ) -> Any: + """ + Calculate the tide constituents for a time series. + + Parameters + ---------- + ts : pd.Series + The time series to analyze. + resample_time : int, optional + The resample time in minutes, by default 30. + lat : float, optional + The latitude of the station, by default 0.0. + **kwargs : Dict[str, Any], optional + Additional keyword arguments to pass to the utide.solve function. + + Returns + ------- + np.ndarray + The tide constituents. + + """ # resample to 30 min for a MUCH faster analysis # https://github.com/wesleybowman/UTide/issues/103 h_rsmp = ts.resample(f"{resample_time}min").mean() @@ -134,9 +154,10 @@ def detide( @param lat: latitude of the station @param resample_time: resample time in minutes @param split_period: period in days to split the time series into (default 365) - @param kwargs: keyword arguments to be passed to utide.reconstruct + @param kwargs: keyword arguments to be passed to calc constituents @return: reconstructed time series """ + verbose = kwargs.get("verbose", False) result_series = [] if constituents is None: constituents = calc_constituents(ts=ts, resample_time=resample_time, lat=lat, **kwargs) @@ -145,11 +166,11 @@ def detide( df = h_rsmp.shift(freq=f"{resample_time / 2}min") for start in range(0, len(df), chunk): - end = min(start + chunk, len(ts)) - ts_chunk = ts.iloc[start:end] + end = min(start + chunk, len(df)) + ts_chunk = df.iloc[start:end] if not ts_chunk.empty: - tidal = utide.reconstruct(ts_chunk.index, nd_format(constituents), verbose=kwargs["verbose"]) + tidal = utide.reconstruct(ts_chunk.index, nd_format(constituents), verbose=verbose) storm_surge = ts_chunk - tidal.h result_series.append(storm_surge) return pd.concat(result_series) @@ -157,24 +178,87 @@ def detide( def tide_analysis( ts: pd.Series[float], - resample_time: int = 10, + resample_time: int = 30, lat: float = 0.0, **kwargs: Dict[str, Any] | None, -) -> Tuple[pd.DataFrame, pd.DataFrame, npt.NDArray[Any]]: +) -> Tuple[pd.Series, pd.Series, npt.NDArray[Any]]: + """ + Perform a tide analysis on a time series. + + Parameters + ---------- + ts : pd.Series + The time series to analyze. + resample_time : int, optional + The resample time in minutes, by default 30. + lat : float, optional + The latitude of the station, by default 0.0. + **kwargs : Dict[str, Any], optional + Additional keyword arguments to pass to the utide.solve function. + + Returns + ------- + Tuple[pd.DataFrame, pd.DataFrame, npt.NDArray[Any]] + A tuple containing the following elements: + + * tide : pd.DataFrame + A dataframe containing the tide values. + * surge : pd.DataFrame + A dataframe containing the surge values. + * constituents : npt.NDArray[Any] + The constituents used in the analysis. + + """ + verbose = kwargs.get("verbose", False) constituents = calc_constituents(ts=ts, lat=lat, resample_time=resample_time, **kwargs) - tidal = utide.reconstruct(ts.index, constituents, **kwargs) - tide = pd.Series(data=tidal.h, index=ts.index) - surge = pd.Series(data=ts.iloc[0, :].values - tidal.h, index=ts.index) + + h_rsmp = ts.resample(f"{resample_time}min").apply(np.nanmean) + df = h_rsmp.shift(freq=f"{resample_time / 2}min") + + tidal = utide.reconstruct(df.index, constituents, verbose=verbose) + tide = pd.Series(data=tidal.h, index=df.index) + surge = pd.Series(data=df.values - tidal.h, index=df.index) return tide, surge, constituents def yearly_tide_analysis( h: pd.Series[float], - resample_time: int = 10, + resample_time: int = 30, split_period: int = 365, lat: int = 0, **kwargs: Dict[str, Any] | None, ) -> Tuple[pd.DataFrame, pd.DataFrame, List[npt.NDArray[Any]], List[int]]: + """ + Perform a tide analysis on a time series, split into yearly intervals. + + Parameters + ---------- + h : pd.Series + The time series to analyze. + resample_time : int, optional + The resample time in minutes, by default 30. + split_period : int, optional + The period in days to split the time series into, by default 365. + lat : int, optional + The latitude of the station, by default 0. + **kwargs : Dict[str, Any], optional + Additional keyword arguments to pass to the utide.solve function. + + Returns + ------- + Tuple[pd.DataFrame, pd.DataFrame, List[npt.NDArray[Any]], List[int]] + A tuple containing the following elements: + + * tide : pd.DataFrame + A dataframe containing the tide values. + * surge : pd.DataFrame + A dataframe containing the surge values. + * coefs : List[npt.NDArray[Any]] + The constituents used in the analysis for each year. + * years : List[int] + The years analyzed. + + """ log = kwargs.get("verbose", False) min_time = pd.Timestamp(h.index.min()) diff --git a/analysea/utils.py b/analysea/utils.py index 4df0b37..a55360f 100644 --- a/analysea/utils.py +++ b/analysea/utils.py @@ -4,6 +4,7 @@ from typing import Any from typing import cast from typing import Dict +from typing import Iterator from typing import Optional from typing import Tuple from typing import Union @@ -20,6 +21,41 @@ # =================== # TIME SERIES # =================== +def resample(df: pd.DataFrame, t_rsp: int = 30) -> pd.DataFrame: + """ + Resample a pandas dataframe to a new time interval. + + @param df (pd.DataFrame): The input dataframe. + @param t_rsp (int): optional, The target resample period in minutes + by default 30. + + @returns (pd.DataFrame): The resampled dataframe. + """ + ts = df.resample(f"{t_rsp}min").mean().shift(freq=f"{int(t_rsp/2)}min") + return ts + + +def interpolate(df: pd.DataFrame, t_rsp: int = 30) -> pd.DataFrame: + """ + This function resamples a pandas dataframe to a new time interval + using linear interpolation. + + It uses analysea's detect_time_step function to interpolate only + between holes in the data and not extrapolate "flat areas" in the + signal + + @param df (pd.DataFrame): The input dataframe. + @param t_rsp (int): optional, The target resample period in minutes + by default 30. + + @returns (pd.DataFrame): The interpolated dataframe. + """ + time_step = detect_time_step(df) + n_interp = int(t_rsp * 60 / time_step.total_seconds()) + ts = df.interpolate(method="linear", limit=n_interp) + return ts + + def detect_splits(sr: pd.Series, max_gap: pd.Timedelta) -> pd.DatetimeIndex: split_points = pd.DatetimeIndex([sr.index[0], sr.index[-1]]) condition = sr.index.to_series().diff() > max_gap @@ -28,7 +64,15 @@ def detect_splits(sr: pd.Series, max_gap: pd.Timedelta) -> pd.DatetimeIndex: return split_points -def split_series(sr: pd.Series, max_gap: pd.Timedelta = pd.Timedelta(hours=24)) -> pd.Series: +def split_series(sr: pd.Series, max_gap: pd.Timedelta = pd.Timedelta(hours=24)) -> Iterator[pd.Series]: + """ + Splits a pandas series into segments without overlapping gaps larger than max_gap. + + @param sr (pd.Series): The input series. + @param max_gap (pd.Timedelta): The maximum allowed gap between two segments. + + @returns: Iterator[pd.Series]: An iterator of segments. + """ for start, stop in itertools.pairwise(detect_splits(sr=sr, max_gap=max_gap)): segment = sr[start:stop] yield segment[:-1] @@ -69,6 +113,26 @@ def cleanup( despike: bool = True, demean: bool = True, ) -> pd.DataFrame: + """ + This function cleans up a time series by removing outliers, + detecting and removing flat areas, and removing steps. + + @param ts (pd.Series): The input time series. + @param clip_limits tuple[float, float]: Optional, The lower and upper + bounds for outlier detection. If None, outlier detection is not performed. + @param kurtosis (float): The threshold for detecting outliers. 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">=3.10,<3.12" -content-hash = "300ec4d5048d362f0f1fdd0622fa14035d1eb521007c3493bd5643a11dfcdca3" +python-versions = ">=3.9,<3.12" +content-hash = "541f721c0ff2fa4ecfcc6fa931e4bc4df2fa502bc2a9efd96c0eeafbb8e46dd7" diff --git a/pyproject.toml b/pyproject.toml index d8a72fa..ddbf524 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -6,28 +6,19 @@ authors = ["Thomas Saillour "] readme = "README.md" [tool.poetry.dependencies] -python = ">=3.10,<3.12" +python = ">=3.9,<3.12" cartopy = "*" dask = {version = "*", extras = ["complete"]} geopandas = "*" matplotlib = "*" -observer = {git = "https://github.com/seareport/observer.git"} pandas = "*" +pytest = "*" ruptures = "*" scikit-image = "*" -searvey = "*" -shapely = "*" -thalassa = "*" +searvey = { git = "https://github.com/oceanmodeling/searvey.git", branch = "dev" } utide = "*" xarray = {version = "*", extras = ["io", "accel"]} -[tool.poetry.group.dev.dependencies] -ipykernel = "*" -ipympl = "*" -jupyter-bokeh = "*" -mypy = "*" -pytest = "*" - [build-system] requires = ["poetry-core"] build-backend = "poetry.core.masonry.api" diff --git a/requirements/requirements-dev.txt b/requirements/requirements-dev.txt index ac228b5..f542ee1 100644 --- a/requirements/requirements-dev.txt +++ b/requirements/requirements-dev.txt @@ -1,6 +1,6 @@ -adlfs==2023.10.0 ; python_version >= "3.10" and python_version < "3.12" \ - --hash=sha256:dfdc8cc782bd78262435fb1bc2a8cfdbdd80342bb1b1ae9dfff968de912b0b09 \ - --hash=sha256:f5cf06c5b0074d17d43838d4c434791a98420d9e768b36a1a02c7b3930686543 +adlfs==2023.12.0 ; python_version >= "3.10" and python_version < "3.12" \ + --hash=sha256:a590694ed9f5a45741e82bff8bcf88c30a790da949310817330b5e7992b8a9e9 \ + --hash=sha256:da6e391afd002c7bb1b75dcc286b78fdf5dbf29aca0f984462033df6311b4e7e aiohttp==3.9.1 ; python_version >= "3.10" and python_version < "3.12" \ --hash=sha256:02ab6006ec3c3463b528374c4cdce86434e7b89ad355e7bf29e2f16b46c7dd6f \ --hash=sha256:04fa38875e53eb7e354ece1607b1d2fdee2d175ea4e4d745f6ec9f751fe20c7c \ @@ -84,9 +84,9 @@ aiosignal==1.3.1 ; python_version >= "3.10" and python_version < "3.12" \ annotated-types==0.6.0 ; python_version >= "3.10" and python_version < "3.12" \ --hash=sha256:0641064de18ba7a25dee8f96403ebc39113d0cb953a01429249d5c7564666a43 \ --hash=sha256:563339e807e53ffd9c267e99fc6d9ea23eb8443c08f112651963e24e22f84a5d -anyio==4.1.0 ; python_version >= "3.10" and python_version < "3.12" \ - --hash=sha256:56a415fbc462291813a94528a779597226619c8e78af7de0507333f700011e5f \ - --hash=sha256:5a0bec7085176715be77df87fc66d6c9d70626bd752fcc85f57cdbee5b3760da +anyio==4.2.0 ; python_version >= "3.10" and python_version < "3.12" \ + --hash=sha256:745843b39e829e108e518c489b31dc757de7d2131d53fac32bd8df268227bfee \ + --hash=sha256:e1875bb4b4e2de1669f4bc7869b6d3f54231cdced71605e6e64c9be77e3be50f appnope==0.1.3 ; python_version >= "3.10" and python_version < "3.12" and platform_system == "Darwin" \ --hash=sha256:02bd91c4de869fbb1e1c50aafc4098827a7a54ab2f39d9dcba6c9547ed920e24 \ --hash=sha256:265a455292d0bd8a72453494fa24df5a11eb18373a60c7c0430889f22548605e @@ -98,9 +98,9 @@ asttokens==2.4.1 ; python_version >= "3.10" and python_version < "3.12" \ async-timeout==4.0.3 ; python_version >= "3.10" and python_version < "3.11" \ --hash=sha256:4640d96be84d82d02ed59ea2b7105a0f7b33abe8703703cd0ab0bf87c427522f \ --hash=sha256:7405140ff1230c310e51dc27b3145b9092d659ce68ff733fb0cefe3ee42be028 -attrs==23.1.0 ; python_version >= "3.10" and python_version < "3.12" \ - --hash=sha256:1f28b4522cdc2fb4256ac1a020c78acf9cba2c6b461ccd2c126f3aa8e8335d04 \ - --hash=sha256:6279836d581513a26f1bf235f9acd333bc9115683f14f7e8fae46c98fc50e015 +attrs==23.2.0 ; python_version >= "3.10" and python_version < "3.12" \ + --hash=sha256:935dc3b529c262f6cf76e50877d35a4bd3c1de194fd41f47a2b7ae8f19971f30 \ + --hash=sha256:99b87a485a5820b23b879f04c2305b44b951b502fd64be915879d77a7e8fc6f1 azure-common==1.1.28 ; python_version >= "3.10" and python_version < "3.12" \ --hash=sha256:4ac0cd3214e36b6a1b6a442686722a5d8cc449603aa833f3f0f40bda836704a3 \ --hash=sha256:5c12d3dcf4ec20599ca6b0d3e09e86e146353d443e7fcc050c9a19c1f9df20ad @@ -134,9 +134,9 @@ beautifulsoup4==4.12.2 ; python_version >= "3.10" and python_version < "3.12" \ bleach==6.1.0 ; python_version >= "3.10" and python_version < "3.12" \ --hash=sha256:0a31f1837963c41d46bbf1331b8778e1308ea0791db03cc4e7357b97cf42a8fe \ --hash=sha256:3225f354cfc436b9789c66c4ee030194bee0568fbf9cbdad3bc8b5c26c5f12b6 -bokeh==3.3.2 ; python_version >= "3.10" and python_version < "3.12" \ - --hash=sha256:ae180f86f776525f7eb81673a1f27e0eb56887d733771662c512ec0d82a433f5 \ - --hash=sha256:f70bcea96c72f85bbeae01b9dcd30a9f27aa48fbfaf8e6f45b6958dfcd08e5c5 +bokeh==3.3.3 ; python_version >= "3.10" and python_version < "3.12" \ + --hash=sha256:6ece6f00263f2d2043a24eaf9db75a6f860efc8a1f96df663186fe3eb2692dd3 \ + --hash=sha256:8f4a95f80e6e03a59eb0ae3c1bce67db3c888a7a5c4f287d4ef9f817849a8b2a bottleneck==1.3.7 ; python_version >= "3.10" and python_version < "3.12" \ --hash=sha256:01e61a512d645193cf4cf4fdacf98d3140c26ace0fcf4c4ed8fdfa366c57e0d1 \ --hash=sha256:1045f9b199bba2675bd7642c25dec5daa21a03a5f0444eb863f2790af52b00b0 \ @@ -385,9 +385,9 @@ colorama==0.4.6 ; python_version >= "3.10" and python_version < "3.12" \ colorcet==3.0.1 ; python_version >= "3.10" and python_version < "3.12" \ --hash=sha256:51455a20353d12fac91f953772d8409f2474e6a0db1af3fa4f7005f405a2480b \ --hash=sha256:8daff01824ee9935fdf762d15c444a67d3e361ad4f8b738ad59ac9bf38f30600 -comm==0.2.0 ; python_version >= "3.10" and python_version < "3.12" \ - --hash=sha256:2da8d9ebb8dd7bfc247adaff99f24dce705638a8042b85cb995066793e391001 \ - --hash=sha256:a517ea2ca28931c7007a7a99c562a0fa5883cfb48963140cf642c41c948498be +comm==0.2.1 ; python_version >= "3.10" and python_version < "3.12" \ + --hash=sha256:0bc91edae1344d39d3661dcbc36937181fdaddb304790458f8b044dbc064b89a \ + 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--hash=sha256:3f687e647ced0d3f2cadbef113c730b6555b9499b62e3a220535438841001c91 \ - --hash=sha256:6cef2fa43815adc8fe895dd3ea473a311249ba0e15e1f7eb9b1b68302339d998 +dask==2024.1.0 ; python_version >= "3.10" and python_version < "3.12" \ + --hash=sha256:717102ef7c309297291095a0061d374f3b72e11ce4e1115ab9faff940e274b4b \ + --hash=sha256:f24fdc7a07e59a1403bf6903e6d8dc15ed6f8607d3311b4f00f88d8a2ac63e49 +dask[complete]==2024.1.0 ; python_version >= "3.10" and python_version < "3.12" \ + --hash=sha256:717102ef7c309297291095a0061d374f3b72e11ce4e1115ab9faff940e274b4b \ + --hash=sha256:f24fdc7a07e59a1403bf6903e6d8dc15ed6f8607d3311b4f00f88d8a2ac63e49 dataretrieval==1.0.6 ; python_version >= "3.10" and python_version < "3.12" \ --hash=sha256:31adec9fd0b8f5b4e620c88bb642ddb2df19477c0ca7e1fe9a5ec604035f64b4 \ --hash=sha256:8f6323b66fd4a5fc800888c85eb350d60335085c3eefc5784b5ed50de6bd37bd @@ -643,52 +643,52 @@ fiona==1.9.5 ; python_version >= "3.10" and python_version < "3.12" \ 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--hash=sha256:a5d77479fb885ef38a16a253a2f4096bc3d14e63a56d6246bfdb56365a12b20c \ + --hash=sha256:a86a5ab2873ed2575d0fcdf1828143cfc6b977ac448e3dc616bb1e3d20efbafa \ + --hash=sha256:ac71e2e201df041a2891067dc36256755b1229ae167edbdc419b16da78732c2f \ + --hash=sha256:b3e1304e5f19ca861d86a72218ecce68f391646d85c851742d265787f55457a4 \ + --hash=sha256:b8be28c036b9f186e8c7eaf8a11b42373e7e4949f9e9f370202b9da4c4c3f56c \ + --hash=sha256:c19044256c44fe299d9a73456aabee4b4d06c6b930287be93b533b4737d70aa1 \ + --hash=sha256:d49ce3ea7b7173faebc5664872243b40cf88814ca3eb135c4a3cdff66af71946 \ + --hash=sha256:e040f905d542362e07e72e03612a6270c33d38281fd573160e1003e43718d68d \ + --hash=sha256:eabae77a07c41ae0b35184894202305c3ad211a93b2eb53837c2a1143c8bc952 \ + --hash=sha256:f791446ff297fd5f1e2247c188de53c1bfb9dd7f0549eba55b73a3c2087a2703 \ + --hash=sha256:f83a4daef6d2a202acb9bf572958f91cfde5b10c8ee7fb1d09a4c81e5d851fd8 frozenlist==1.4.1 ; python_version >= "3.10" and python_version < "3.12" \ --hash=sha256:04ced3e6a46b4cfffe20f9ae482818e34eba9b5fb0ce4056e4cc9b6e212d09b7 \ --hash=sha256:0633c8d5337cb5c77acbccc6357ac49a1770b8c487e5b3505c57b949b4b82e98 \ @@ -772,9 +772,9 @@ fsspec==2023.12.2 ; python_version >= "3.10" and python_version < "3.12" \ --hash=sha256:d800d87f72189a745fa3d6b033b9dc4a34ad069f60ca60b943a63599f5501960 future==0.18.3 ; python_version >= "3.10" and python_version < "3.12" \ --hash=sha256:34a17436ed1e96697a86f9de3d15a3b0be01d8bc8de9c1dffd59fb8234ed5307 -geopandas==0.14.1 ; python_version >= "3.10" and python_version < "3.12" \ - --hash=sha256:4853ff89ecb6d1cfc43e7b3671092c8160e8a46a3dd7368f25906283314e42bb \ - --hash=sha256:ed5a7cae7874bfc3238fb05e0501cc1760e1b7b11e5b76ecad29da644ca305da +geopandas==0.14.2 ; python_version >= "3.10" and python_version < "3.12" \ + --hash=sha256:0efa61235a68862c1c6be89fc3707cdeba67667d5676bb19e24f3c57a8c2f723 \ + --hash=sha256:6e71d57b8376f9fdc9f1c3aa3170e7e420e91778de854f51013ae66fd371ccdb geoviews==1.11.0 ; python_version >= "3.10" and python_version < "3.12" \ --hash=sha256:2c4d09554019867adfa4ea655617ce8bb9ffa08b534a7e591f961dc6078cffcc \ --hash=sha256:d54a838708b72936fdcbe3eb9e170f977fa7d3733a45a200c9ae322841d4373a @@ -819,36 +819,36 @@ html5lib==1.1 ; python_version >= "3.10" and python_version < "3.12" \ httpcore==1.0.2 ; python_version >= "3.10" and python_version < "3.12" \ --hash=sha256:096cc05bca73b8e459a1fc3dcf585148f63e534eae4339559c9b8a8d6399acc7 \ --hash=sha256:9fc092e4799b26174648e54b74ed5f683132a464e95643b226e00c2ed2fa6535 -httpx==0.25.2 ; python_version >= "3.10" and python_version < "3.12" \ - --hash=sha256:8b8fcaa0c8ea7b05edd69a094e63a2094c4efcb48129fb757361bc423c0ad9e8 \ - --hash=sha256:a05d3d052d9b2dfce0e3896636467f8a5342fb2b902c819428e1ac65413ca118 +httpx==0.26.0 ; python_version >= "3.10" and python_version < "3.12" \ + --hash=sha256:451b55c30d5185ea6b23c2c793abf9bb237d2a7dfb901ced6ff69ad37ec1dfaf \ + --hash=sha256:8915f5a3627c4d47b73e8202457cb28f1266982d1159bd5779d86a80c0eab1cd idna==3.6 ; python_version >= "3.10" and python_version < "3.12" \ --hash=sha256:9ecdbbd083b06798ae1e86adcbfe8ab1479cf864e4ee30fe4e46a003d12491ca \ --hash=sha256:c05567e9c24a6b9faaa835c4821bad0590fbb9d5779e7caa6e1cc4978e7eb24f imageio==2.33.1 ; python_version >= "3.10" and python_version < "3.12" \ --hash=sha256:78722d40b137bd98f5ec7312119f8aea9ad2049f76f434748eb306b6937cc1ce \ --hash=sha256:c5094c48ccf6b2e6da8b4061cd95e1209380afafcbeae4a4e280938cce227e1d -importlib-metadata==7.0.0 ; python_version >= "3.10" and python_version < "3.12" \ - --hash=sha256:7fc841f8b8332803464e5dc1c63a2e59121f46ca186c0e2e182e80bf8c1319f7 \ - --hash=sha256:d97503976bb81f40a193d41ee6570868479c69d5068651eb039c40d850c59d67 +importlib-metadata==7.0.1 ; python_version >= "3.10" and python_version < "3.12" \ + --hash=sha256:4805911c3a4ec7c3966410053e9ec6a1fecd629117df5adee56dfc9432a1081e \ + --hash=sha256:f238736bb06590ae52ac1fab06a3a9ef1d8dce2b7a35b5ab329371d6c8f5d2cc importlib-resources==6.1.1 ; python_version >= "3.10" and python_version < "3.12" \ --hash=sha256:3893a00122eafde6894c59914446a512f728a0c1a45f9bb9b63721b6bacf0b4a \ --hash=sha256:e8bf90d8213b486f428c9c39714b920041cb02c184686a3dee24905aaa8105d6 iniconfig==2.0.0 ; python_version >= "3.10" and python_version < "3.12" \ --hash=sha256:2d91e135bf72d31a410b17c16da610a82cb55f6b0477d1a902134b24a455b8b3 \ --hash=sha256:b6a85871a79d2e3b22d2d1b94ac2824226a63c6b741c88f7ae975f18b6778374 -ipykernel==6.27.1 ; python_version >= "3.10" and python_version < "3.12" \ - --hash=sha256:7d5d594b6690654b4d299edba5e872dc17bb7396a8d0609c97cb7b8a1c605de6 \ - --hash=sha256:dab88b47f112f9f7df62236511023c9bdeef67abc73af7c652e4ce4441601686 +ipykernel==6.28.0 ; python_version >= "3.10" and python_version < "3.12" \ + --hash=sha256:69c11403d26de69df02225916f916b37ea4b9af417da0a8c827f84328d88e5f3 \ + --hash=sha256:c6e9a9c63a7f4095c0a22a79f765f079f9ec7be4f2430a898ddea889e8665661 ipympl==0.9.3 ; python_version >= "3.10" and python_version < "3.12" \ --hash=sha256:49bab75c05673a6881d1aaec5d8ac81d4624f73d292d154c5fb7096f10236a2b \ --hash=sha256:d113cd55891bafe9b27ef99b6dd111a87beb6bb2ae550c404292272103be8013 ipython-genutils==0.2.0 ; python_version >= "3.10" and python_version < "3.12" \ --hash=sha256:72dd37233799e619666c9f639a9da83c34013a73e8bbc79a7a6348d93c61fab8 \ --hash=sha256:eb2e116e75ecef9d4d228fdc66af54269afa26ab4463042e33785b887c628ba8 -ipython==8.18.1 ; python_version >= "3.10" and python_version < "3.12" \ - --hash=sha256:ca6f079bb33457c66e233e4580ebfc4128855b4cf6370dddd73842a9563e8a27 \ - --hash=sha256:e8267419d72d81955ec1177f8a29aaa90ac80ad647499201119e2f05e99aa397 +ipython==8.20.0 ; python_version >= "3.10" and python_version < "3.12" \ + --hash=sha256:2f21bd3fc1d51550c89ee3944ae04bbc7bc79e129ea0937da6e6c68bfdbf117a \ + --hash=sha256:bc9716aad6f29f36c449e30821c9dd0c1c1a7b59ddcc26931685b87b4c569619 ipywidgets==8.1.1 ; python_version >= "3.10" and python_version < "3.12" \ --hash=sha256:2b88d728656aea3bbfd05d32c747cfd0078f9d7e159cf982433b58ad717eed7f \ --hash=sha256:40211efb556adec6fa450ccc2a77d59ca44a060f4f9f136833df59c9f538e6e8 @@ -858,18 +858,18 @@ isodate==0.6.1 ; python_version >= "3.10" and python_version < "3.12" \ jedi==0.19.1 ; python_version >= "3.10" and python_version < "3.12" \ --hash=sha256:cf0496f3651bc65d7174ac1b7d043eff454892c708a87d1b683e57b569927ffd \ --hash=sha256:e983c654fe5c02867aef4cdfce5a2fbb4a50adc0af145f70504238f18ef5e7e0 -jinja2==3.1.2 ; python_version >= "3.10" and python_version < "3.12" \ - --hash=sha256:31351a702a408a9e7595a8fc6150fc3f43bb6bf7e319770cbc0db9df9437e852 \ - --hash=sha256:6088930bfe239f0e6710546ab9c19c9ef35e29792895fed6e6e31a023a182a61 +jinja2==3.1.3 ; python_version >= "3.10" and python_version < "3.12" \ + --hash=sha256:7d6d50dd97d52cbc355597bd845fabfbac3f551e1f99619e39a35ce8c370b5fa \ + --hash=sha256:ac8bd6544d4bb2c9792bf3a159e80bba8fda7f07e81bc3aed565432d5925ba90 jupyter-bokeh==3.0.7 ; python_version >= "3.10" and python_version < "3.12" \ --hash=sha256:2da8c3ddc734d15737bf06126d9e31e84d30f18ac3da3a3f95be40a95a054c87 \ --hash=sha256:676d74bd8b95c7467d5e7ea1c954b306c7768b7bfa2bb3dd32e64efdf7dc09ee jupyter-client==8.6.0 ; python_version >= "3.10" and python_version < "3.12" \ --hash=sha256:0642244bb83b4764ae60d07e010e15f0e2d275ec4e918a8f7b80fbbef3ca60c7 \ --hash=sha256:909c474dbe62582ae62b758bca86d6518c85234bdee2d908c778db6d72f39d99 -jupyter-core==5.5.0 ; python_version >= "3.10" and python_version < "3.12" \ - --hash=sha256:880b86053bf298a8724994f95e99b99130659022a4f7f45f563084b6223861d3 \ - --hash=sha256:e11e02cd8ae0a9de5c6c44abf5727df9f2581055afe00b22183f621ba3585805 +jupyter-core==5.7.1 ; python_version >= "3.10" and python_version < "3.12" \ + --hash=sha256:c65c82126453a723a2804aa52409930434598fd9d35091d63dfb919d2b765bb7 \ + --hash=sha256:de61a9d7fc71240f688b2fb5ab659fbb56979458dc66a71decd098e03c79e218 jupyterlab-widgets==3.0.9 ; python_version >= "3.10" and python_version < "3.12" 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--hash=sha256:d9b338c19fa2412f76e17525c1b4f2c687a55b156320acb588df79f2e6fa9fea \ - --hash=sha256:ee5d62d28b854eb61889cde4e1dbc10fbaa5560cb39780c3995f6737f7e82120 \ - --hash=sha256:f2c2521a8e4d6d769e3234350ba7b65ff5d527137cdcde13ff4d99114b0c8e7d \ - --hash=sha256:f6efc9bd72258f89a3816e3a98c09d36f079c223aa345c659622f056b760ab42 \ - --hash=sha256:f7c5d642db47376a0cc130f0de6d055056e010debdaf0707cd2b0fc7e7ef30ea \ - --hash=sha256:fcb6d9afb1b6208b4c712af0dafdc650f518836065df0d4fb1d800f5d6773db2 \ - --hash=sha256:fcd2572dd4519e8a6642b733cd3a8cfc1ef94bafd0c1ceed9c94fe736cb65b6a +mypy==1.8.0 ; python_version >= "3.10" and python_version < "3.12" \ + --hash=sha256:028cf9f2cae89e202d7b6593cd98db6759379f17a319b5faf4f9978d7084cdc6 \ + --hash=sha256:2afecd6354bbfb6e0160f4e4ad9ba6e4e003b767dd80d85516e71f2e955ab50d \ + --hash=sha256:2b5b6c721bd4aabaadead3a5e6fa85c11c6c795e0c81a7215776ef8afc66de02 \ + --hash=sha256:42419861b43e6962a649068a61f4a4839205a3ef525b858377a960b9e2de6e0d \ + --hash=sha256:42c6680d256ab35637ef88891c6bd02514ccb7e1122133ac96055ff458f93fc3 \ + --hash=sha256:485a8942f671120f76afffff70f259e1cd0f0cfe08f81c05d8816d958d4577d3 \ + --hash=sha256:4c886c6cce2d070bd7df4ec4a05a13ee20c0aa60cb587e8d1265b6c03cf91da3 \ + --hash=sha256:4e6d97288757e1ddba10dd9549ac27982e3e74a49d8d0179fc14d4365c7add66 \ + --hash=sha256:4ef4be7baf08a203170f29e89d79064463b7fc7a0908b9d0d5114e8009c3a259 \ + --hash=sha256:51720c776d148bad2372ca21ca29256ed483aa9a4cdefefcef49006dff2a6835 \ + --hash=sha256:52825b01f5c4c1c4eb0db253ec09c7aa17e1a7304d247c48b6f3599ef40db8bd \ + --hash=sha256:538fd81bb5e430cc1381a443971c0475582ff9f434c16cd46d2c66763ce85d9d \ + --hash=sha256:5c1538c38584029352878a0466f03a8ee7547d7bd9f641f57a0f3017a7c905b8 \ + --hash=sha256:6ff8b244d7085a0b425b56d327b480c3b29cafbd2eff27316a004f9a7391ae07 \ + --hash=sha256:7178def594014aa6c35a8ff411cf37d682f428b3b5617ca79029d8ae72f5402b \ + --hash=sha256:720a5ca70e136b675af3af63db533c1c8c9181314d207568bbe79051f122669e \ + --hash=sha256:7f1478736fcebb90f97e40aff11a5f253af890c845ee0c850fe80aa060a267c6 \ + --hash=sha256:855fe27b80375e5c5878492f0729540db47b186509c98dae341254c8f45f42ae \ + --hash=sha256:8963b83d53ee733a6e4196954502b33567ad07dfd74851f32be18eb932fb1cb9 \ + --hash=sha256:9261ed810972061388918c83c3f5cd46079d875026ba97380f3e3978a72f503d \ + --hash=sha256:99b00bc72855812a60d253420d8a2eae839b0afa4938f09f4d2aa9bb4654263a \ + --hash=sha256:ab3c84fa13c04aeeeabb2a7f67a25ef5d77ac9d6486ff33ded762ef353aa5592 \ + --hash=sha256:afe3fe972c645b4632c563d3f3eff1cdca2fa058f730df2b93a35e3b0c538218 \ + --hash=sha256:d19c413b3c07cbecf1f991e2221746b0d2a9410b59cb3f4fb9557f0365a1a817 \ + --hash=sha256:df9824ac11deaf007443e7ed2a4a26bebff98d2bc43c6da21b2b64185da011c4 \ + --hash=sha256:e46f44b54ebddbeedbd3d5b289a893219065ef805d95094d16a0af6630f5d410 \ + --hash=sha256:f5ac9a4eeb1ec0f1ccdc6f326bcdb464de5f80eb07fb38b5ddd7b0de6bc61e55 nest-asyncio==1.5.8 ; python_version >= "3.10" and python_version < "3.12" \ --hash=sha256:25aa2ca0d2a5b5531956b9e273b45cf664cae2b145101d73b86b199978d48fdb \ --hash=sha256:accda7a339a70599cb08f9dd09a67e0c2ef8d8d6f4c07f96ab203f2ae254e48d @@ -1423,9 +1410,9 @@ numba==0.58.1 ; python_version >= "3.10" and python_version < "3.12" \ --hash=sha256:d3e2fe81fe9a59fcd99cc572002101119059d64d31eb6324995ee8b0f144a306 \ --hash=sha256:e63d6aacaae1ba4ef3695f1c2122b30fa3d8ba039c8f517784668075856d79e2 \ --hash=sha256:ea5bfcf7d641d351c6a80e8e1826eb4a145d619870016eeaf20bbd71ef5caa22 -numbagg==0.6.7 ; python_version >= "3.10" and python_version < "3.12" \ - --hash=sha256:c978e34e9e68775cf7bdd2380e0a93fd27213df0c35d71be180bd47c77a64fc6 \ - --hash=sha256:fc70b14367254a99bfa279d2ce17aa9fa9ccd5982458f605d0d2b50045c60219 +numbagg==0.6.8 ; python_version >= "3.10" and python_version < "3.12" \ + --hash=sha256:9e9f772bf0a6dc16544f3a3191696999576aabf4559e00068e40ac9a514b91a3 \ + --hash=sha256:aed13cd7e370687a6aa9d3b7688702fb0502667f8867b51cfd8160591957063d numcodecs==0.12.1 ; python_version >= "3.10" and python_version < "3.12" \ --hash=sha256:05d91a433733e7eef268d7e80ec226a0232da244289614a8f3826901aec1098e \ --hash=sha256:0e79bf9d1d37199ac00a60ff3adb64757523291d19d03116832e600cac391c51 \ @@ -1453,43 +1440,43 @@ numpy-groupies==0.10.2 ; python_version >= "3.10" and python_version < "3.12" \ --hash=sha256:f920c4ded899f5975d94fc63d634e7c89622056bbab8cc98a44d4320a0ae8a12 numpy-indexed==0.3.7 ; python_version >= "3.10" and python_version < "3.12" \ --hash=sha256:3e9f8f5ca453e49809618b3717b8ce07551b616a4ae43069c46aaad286386a9e -numpy==1.26.2 ; python_version >= "3.10" and python_version < "3.12" \ - --hash=sha256:06fa1ed84aa60ea6ef9f91ba57b5ed963c3729534e6e54055fc151fad0423f0a \ - --hash=sha256:174a8880739c16c925799c018f3f55b8130c1f7c8e75ab0a6fa9d41cab092fd6 \ - --hash=sha256:1a13860fdcd95de7cf58bd6f8bc5a5ef81c0b0625eb2c9a783948847abbef2c2 \ - --hash=sha256:1cc3d5029a30fb5f06704ad6b23b35e11309491c999838c31f124fee32107c79 \ - 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--hash=sha256:854ab91a2906ef29dc3925a064fcd365c7b4da743f84b123002f6139bcb3f8a7 \ - --hash=sha256:94cc3c222bb9fb5a12e334d0479b97bb2df446fbe622b470928f5284ffca3f8d \ - --hash=sha256:96ca5482c3dbdd051bcd1fce8034603d6ebfc125a7bd59f55b40d8f5d246832b \ - --hash=sha256:a2bbc29fcb1771cd7b7425f98b05307776a6baf43035d3b80c4b0f29e9545186 \ - --hash=sha256:a4cd6ed4a339c21f1d1b0fdf13426cb3b284555c27ac2f156dfdaaa7e16bfab0 \ - --hash=sha256:aa18428111fb9a591d7a9cc1b48150097ba6a7e8299fb56bdf574df650e7d1f1 \ - --hash=sha256:aa317b2325f7aa0a9471663e6093c210cb2ae9c0ad824732b307d2c51983d5b6 \ - --hash=sha256:b04f5dc6b3efdaab541f7857351aac359e6ae3c126e2edb376929bd3b7f92d7e \ - --hash=sha256:b272d4cecc32c9e19911891446b72e986157e6a1809b7b56518b4f3755267523 \ - --hash=sha256:b361d369fc7e5e1714cf827b731ca32bff8d411212fccd29ad98ad622449cc36 \ - --hash=sha256:b96e7b9c624ef3ae2ae0e04fa9b460f6b9f17ad8b4bec6d7756510f1f6c0c841 \ - --hash=sha256:baf8aab04a2c0e859da118f0b38617e5ee65d75b83795055fb66c0d5e9e9b818 \ - --hash=sha256:bcc008217145b3d77abd3e4d5ef586e3bdfba8fe17940769f8aa09b99e856c00 \ - --hash=sha256:bd3f0091e845164a20bd5a326860c840fe2af79fa12e0469a12768a3ec578d80 \ - --hash=sha256:cc392fdcbd21d4be6ae1bb4475a03ce3b025cd49a9be5345d76d7585aea69440 \ - --hash=sha256:d73a3abcac238250091b11caef9ad12413dab01669511779bc9b29261dd50210 \ - --hash=sha256:f43740ab089277d403aa07567be138fc2a89d4d9892d113b76153e0e412409f8 \ - --hash=sha256:f65738447676ab5777f11e6bbbdb8ce11b785e105f690bc45966574816b6d3ea \ - --hash=sha256:f79b231bf5c16b1f39c7f4875e1ded36abee1591e98742b05d8a0fb55d8a3eec \ - --hash=sha256:fe6b44fb8fcdf7eda4ef4461b97b3f63c466b27ab151bec2366db8b197387841 +numpy==1.26.3 ; python_version >= "3.10" and python_version < "3.12" \ + --hash=sha256:02f98011ba4ab17f46f80f7f8f1c291ee7d855fcef0a5a98db80767a468c85cd \ + --hash=sha256:0b7e807d6888da0db6e7e75838444d62495e2b588b99e90dd80c3459594e857b \ + --hash=sha256:12c70ac274b32bc00c7f61b515126c9205323703abb99cd41836e8125ea0043e \ + --hash=sha256:1666f634cb3c80ccbd77ec97bc17337718f56d6658acf5d3b906ca03e90ce87f \ + --hash=sha256:18c3319a7d39b2c6a9e3bb75aab2304ab79a811ac0168a671a62e6346c29b03f \ + --hash=sha256:211ddd1e94817ed2d175b60b6374120244a4dd2287f4ece45d49228b4d529178 \ + --hash=sha256:21a9484e75ad018974a2fdaa216524d64ed4212e418e0a551a2d83403b0531d3 \ + --hash=sha256:39763aee6dfdd4878032361b30b2b12593fb445ddb66bbac802e2113eb8a6ac4 \ + --hash=sha256:3c67423b3703f8fbd90f5adaa37f85b5794d3366948efe9a5190a5f3a83fc34e \ + --hash=sha256:46f47ee566d98849323f01b349d58f2557f02167ee301e5e28809a8c0e27a2d0 \ + --hash=sha256:51c7f1b344f302067b02e0f5b5d2daa9ed4a721cf49f070280ac202738ea7f00 \ + --hash=sha256:5f24750ef94d56ce6e33e4019a8a4d68cfdb1ef661a52cdaee628a56d2437419 \ + --hash=sha256:697df43e2b6310ecc9d95f05d5ef20eacc09c7c4ecc9da3f235d39e71b7da1e4 \ + --hash=sha256:6d45b3ec2faed4baca41c76617fcdcfa4f684ff7a151ce6fc78ad3b6e85af0a6 \ + --hash=sha256:77810ef29e0fb1d289d225cabb9ee6cf4d11978a00bb99f7f8ec2132a84e0166 \ + --hash=sha256:7ca4f24341df071877849eb2034948459ce3a07915c2734f1abb4018d9c49d7b \ + --hash=sha256:7f784e13e598e9594750b2ef6729bcd5a47f6cfe4a12cca13def35e06d8163e3 \ + --hash=sha256:806dd64230dbbfaca8a27faa64e2f414bf1c6622ab78cc4264f7f5f028fee3bf \ + --hash=sha256:867e3644e208c8922a3be26fc6bbf112a035f50f0a86497f98f228c50c607bb2 \ + --hash=sha256:8c66d6fec467e8c0f975818c1796d25c53521124b7cfb760114be0abad53a0a2 \ + --hash=sha256:8ed07a90f5450d99dad60d3799f9c03c6566709bd53b497eb9ccad9a55867f36 \ + --hash=sha256:9bc6d1a7f8cedd519c4b7b1156d98e051b726bf160715b769106661d567b3f03 \ + --hash=sha256:9e1591f6ae98bcfac2a4bbf9221c0b92ab49762228f38287f6eeb5f3f55905ce \ + --hash=sha256:9e87562b91f68dd8b1c39149d0323b42e0082db7ddb8e934ab4c292094d575d6 \ + --hash=sha256:a7081fd19a6d573e1a05e600c82a1c421011db7935ed0d5c483e9dd96b99cf13 \ + --hash=sha256:a8474703bffc65ca15853d5fd4d06b18138ae90c17c8d12169968e998e448bb5 \ + --hash=sha256:af36e0aa45e25c9f57bf684b1175e59ea05d9a7d3e8e87b7ae1a1da246f2767e \ + --hash=sha256:b1240f767f69d7c4c8a29adde2310b871153df9b26b5cb2b54a561ac85146485 \ + --hash=sha256:b4d362e17bcb0011738c2d83e0a65ea8ce627057b2fdda37678f4374a382a137 \ + --hash=sha256:b831295e5472954104ecb46cd98c08b98b49c69fdb7040483aff799a755a7374 \ + --hash=sha256:b8c275f0ae90069496068c714387b4a0eba5d531aace269559ff2b43655edd58 \ + --hash=sha256:bdd2b45bf079d9ad90377048e2747a0c82351989a2165821f0c96831b4a2a54b \ + --hash=sha256:cc0743f0302b94f397a4a65a660d4cd24267439eb16493fb3caad2e4389bccbb \ + --hash=sha256:da4b0c6c699a0ad73c810736303f7fbae483bcb012e38d7eb06a5e3b432c981b \ + --hash=sha256:f25e2811a9c932e43943a2615e65fc487a0b6b49218899e62e426e7f0a57eeda \ + --hash=sha256:f73497e8c38295aaa4741bdfa4fda1a5aedda5473074369eca10626835445511 observer @ git+https://github.com/seareport/observer.git@ce6e676030573560e8314abeee92ee3737a6ffab ; python_version >= "3.10" and python_version < "3.12" opt-einsum==3.3.0 ; python_version >= "3.10" and python_version < "3.12" \ --hash=sha256:2455e59e3947d3c275477df7f5205b30635e266fe6dc300e3d9f9646bfcea147 \ @@ -1523,9 +1510,9 @@ pandas==2.1.4 ; python_version >= "3.10" and python_version < "3.12" \ --hash=sha256:f237e6ca6421265643608813ce9793610ad09b40154a3344a088159590469e46 \ --hash=sha256:f69b0c9bb174a2342818d3e2778584e18c740d56857fc5cdb944ec8bbe4082cf \ --hash=sha256:fcb68203c833cc735321512e13861358079a96c174a61f5116a1de89c58c0ef7 -panel==1.3.4 ; python_version >= "3.10" and python_version < "3.12" \ - --hash=sha256:14afde92a5c2b531f2ccb785b34b4711e1f4557934c85727b3594e2dae73d0a5 \ - --hash=sha256:96c3c99e987b22d69777e50d3534c7635d75503842d6bff6e0042957e0ad90a1 +panel==1.3.6 ; python_version >= "3.10" and python_version < "3.12" \ + --hash=sha256:d772ade53081d48ab5b58c82e7d65962edae5aef07707257e21277d6f4b3fd04 \ + --hash=sha256:e3de0785b6f5098f8b36dfebb8c30d562146f8450107517b3374778619449a70 param==2.0.1 ; python_version >= "3.10" and python_version < "3.12" \ --hash=sha256:4bfc94c0e4127626fa833e30c71c91ea73e7675b80c26dbdd4a6e5a8f6dc46db \ --hash=sha256:7943a04607822efd46e96e1827dc5fa929a2fc3b1fe9fc7b7dca7d17a8031a5b @@ -1538,61 +1525,75 @@ partd==1.4.1 ; python_version >= "3.10" and python_version < "3.12" \ pexpect==4.9.0 ; python_version >= "3.10" and python_version < "3.12" and sys_platform != "win32" \ --hash=sha256:7236d1e080e4936be2dc3e326cec0af72acf9212a7e1d060210e70a47e253523 \ --hash=sha256:ee7d41123f3c9911050ea2c2dac107568dc43b2d3b0c7557a33212c398ead30f -pillow==10.1.0 ; python_version >= "3.10" and python_version < "3.12" \ - --hash=sha256:00f438bb841382b15d7deb9a05cc946ee0f2c352653c7aa659e75e592f6fa17d \ - --hash=sha256:0248f86b3ea061e67817c47ecbe82c23f9dd5d5226200eb9090b3873d3ca32de \ - --hash=sha256:04f6f6149f266a100374ca3cc368b67fb27c4af9f1cc8cb6306d849dcdf12616 \ - --hash=sha256:062a1610e3bc258bff2328ec43f34244fcec972ee0717200cb1425214fe5b839 \ - --hash=sha256:0a026c188be3b443916179f5d04548092e253beb0c3e2ee0a4e2cdad72f66099 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--hash=sha256:cb28c753fd5eb3dd859b4ee95de66cc62af91bcff5db5f2571d32a520baf1f04 \ + --hash=sha256:cb4c38abeef13c61d6916f264d4845fab99d7b711be96c326b84df9e3e0ff62d \ + --hash=sha256:d1b35bcd6c5543b9cb547dee3150c93008f8dd0f1fef78fc0cd2b141c5baf58a \ + --hash=sha256:d8e6aeb9201e655354b3ad049cb77d19813ad4ece0df1249d3c793de3774f8c7 \ + --hash=sha256:d8ecd059fdaf60c1963c58ceb8997b32e9dc1b911f5da5307aab614f1ce5c2fb \ + --hash=sha256:da2b52b37dad6d9ec64e653637a096905b258d2fc2b984c41ae7d08b938a67e4 \ + --hash=sha256:e87f0b2c78157e12d7686b27d63c070fd65d994e8ddae6f328e0dcf4a0cd007e \ + --hash=sha256:edca80cbfb2b68d7b56930b84a0e45ae1694aeba0541f798e908a49d66b837f1 \ + --hash=sha256:f379abd2f1e3dddb2b61bc67977a6b5a0a3f7485538bcc6f39ec76163891ee48 \ + --hash=sha256:fe4c15f6c9285dc54ce6553a3ce908ed37c8f3825b5a51a15c91442bb955b868 platformdirs==4.1.0 ; python_version >= "3.10" and python_version < "3.12" \ --hash=sha256:11c8f37bcca40db96d8144522d925583bdb7a31f7b0e37e3ed4318400a8e2380 \ --hash=sha256:906d548203468492d432bcb294d4bc2fff751bf84971fbb2c10918cc206ee420 @@ -1608,23 +1609,23 @@ portalocker==2.8.2 ; python_version >= "3.10" and python_version < "3.12" \ prompt-toolkit==3.0.43 ; python_version >= "3.10" and python_version < "3.12" \ --hash=sha256:3527b7af26106cbc65a040bcc84839a3566ec1b051bb0bfe953631e704b0ff7d \ --hash=sha256:a11a29cb3bf0a28a387fe5122cdb649816a957cd9261dcedf8c9f1fef33eacf6 -psutil==5.9.6 ; python_version >= "3.10" and python_version < "3.12" \ - --hash=sha256:10e8c17b4f898d64b121149afb136c53ea8b68c7531155147867b7b1ac9e7e28 \ - --hash=sha256:18cd22c5db486f33998f37e2bb054cc62fd06646995285e02a51b1e08da97017 \ - --hash=sha256:3ebf2158c16cc69db777e3c7decb3c0f43a7af94a60d72e87b2823aebac3d602 \ - --hash=sha256:51dc3d54607c73148f63732c727856f5febec1c7c336f8f41fcbd6315cce76ac \ - --hash=sha256:6e5fb8dc711a514da83098bc5234264e551ad980cec5f85dabf4d38ed6f15e9a \ - --hash=sha256:70cb3beb98bc3fd5ac9ac617a327af7e7f826373ee64c80efd4eb2856e5051e9 \ - 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--hash=sha256:f37f87e4d73b79e6c5e749440c3113b81d1ee7d26f21c19c47371ddea834f414 \ + --hash=sha256:fe361f743cb3389b8efda21980d93eb55c1f1e3898269bc9a2a1d0bb7b1f6508 \ + --hash=sha256:fe8b7f07948f1304497ce4f4684881250cd859b16d06a1dc4d7941eeb6233bfe ptyprocess==0.7.0 ; python_version >= "3.10" and python_version < "3.12" and sys_platform != "win32" \ --hash=sha256:4b41f3967fce3af57cc7e94b888626c18bf37a083e3651ca8feeb66d492fef35 \ --hash=sha256:5c5d0a3b48ceee0b48485e0c26037c0acd7d29765ca3fbb5cb3831d347423220 @@ -1634,161 +1635,161 @@ pure-eval==0.2.2 ; python_version >= "3.10" and python_version < "3.12" \ pyarrow-hotfix==0.6 ; python_version >= "3.10" and python_version < "3.12" \ --hash=sha256:79d3e030f7ff890d408a100ac16d6f00b14d44a502d7897cd9fc3e3a534e9945 \ --hash=sha256:dcc9ae2d220dff0083be6a9aa8e0cdee5182ad358d4931fce825c545e5c89178 -pyarrow==14.0.1 ; python_version >= "3.10" and python_version < "3.12" \ - --hash=sha256:0140c7e2b740e08c5a459439d87acd26b747fc408bde0a8806096ee0baaa0c15 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--hash=sha256:e33b0834f1cf779aa839975f9d8755a7c2420510c0fa1e9fa0497de77cd35d2c \ + --hash=sha256:e4ace1e220b078c8e48e82c081e35002038657e4b37d403ce940fa679e57113b \ + --hash=sha256:e4cf2d5829f6963a5483ec01578ee76d329eb5caf330ecd05b3edd697e7d768a \ + --hash=sha256:e574de99d735b3fc8364cba9912c2bec2da78775eba95cbb225ef7dda6acea24 \ + --hash=sha256:e646c0e282e960345314f42f2cea5e0b5f56938c093541ea6dbf11aec2862391 \ + --hash=sha256:e8a5ac97ea521d7bde7621d86c30e86b798cdecd985723c4ed737a2aa9e77d0c \ + --hash=sha256:eedf97be7bc3dbc8addcef4142f4b4164066df0c6f36397ae4aaed3eb187d8ab \ + --hash=sha256:ef633add81832f4b56d3b4c9408b43d530dfca29e68fb1b797dcb861a2c734cd \ + --hash=sha256:f27207e8ca3e5e021e2402ba942e5b4c629718e665c81b8b306f3c8b1ddbb786 \ + --hash=sha256:f85f3843bdb1fe80e8c206fe6eed7a1caeae897e496542cee499c374a85c6e08 \ + --hash=sha256:f8e81e4b55930e5ffab4a68db1af431629cf2e4066dbdbfef65348b8ab804ea8 \ + --hash=sha256:f96ae96a060a8072ceff4cfde89d261837b4294a4f28b84a28765470d502ccc6 \ + --hash=sha256:fd9e98b408384989ea4ab60206b8e100d8687da18b5c813c11e92fd8212a98e0 \ + --hash=sha256:ffff855100bc066ff2cd3aa4a60bc9534661816b110f0243e59503ec2df38421 pydantic-settings==2.1.0 ; python_version >= "3.10" and python_version < "3.12" \ --hash=sha256:26b1492e0a24755626ac5e6d715e9077ab7ad4fb5f19a8b7ed7011d52f36141c \ --hash=sha256:7621c0cb5d90d1140d2f0ef557bdf03573aac7035948109adf2574770b77605a -pydantic==2.5.2 ; python_version >= "3.10" and python_version < "3.12" \ - --hash=sha256:80c50fb8e3dcecfddae1adbcc00ec5822918490c99ab31f6cf6140ca1c1429f0 \ - --hash=sha256:ff177ba64c6faf73d7afa2e8cad38fd456c0dbe01c9954e71038001cd15a6edd +pydantic==2.5.3 ; python_version >= "3.10" and python_version < "3.12" \ + --hash=sha256:b3ef57c62535b0941697cce638c08900d87fcb67e29cfa99e8a68f747f393f7a \ + --hash=sha256:d0caf5954bee831b6bfe7e338c32b9e30c85dfe080c843680783ac2b631673b4 pygments==2.17.2 ; python_version >= "3.10" and python_version < "3.12" \ --hash=sha256:b27c2826c47d0f3219f29554824c30c5e8945175d888647acd804ddd04af846c \ --hash=sha256:da46cec9fd2de5be3a8a784f434e4c4ab670b4ff54d605c4c2717e9d49c4c367 @@ -1829,9 +1830,9 @@ pyproj==3.6.1 ; python_version >= "3.10" and python_version < "3.12" \ pyshp==2.3.1 ; python_version >= "3.10" and python_version < "3.12" \ --hash=sha256:4caec82fd8dd096feba8217858068bacb2a3b5950f43c048c6dc32a3489d5af1 \ --hash=sha256:67024c0ccdc352ba5db777c4e968483782dfa78f8e200672a90d2d30fd8b7b49 -pytest==7.4.3 ; python_version >= "3.10" and python_version < "3.12" \ - --hash=sha256:0d009c083ea859a71b76adf7c1d502e4bc170b80a8ef002da5806527b9591fac \ - --hash=sha256:d989d136982de4e3b29dabcc838ad581c64e8ed52c11fbe86ddebd9da0818cd5 +pytest==7.4.4 ; python_version >= "3.10" and python_version < "3.12" \ + --hash=sha256:2cf0005922c6ace4a3e2ec8b4080eb0d9753fdc93107415332f50ce9e7994280 \ + --hash=sha256:b090cdf5ed60bf4c45261be03239c2c1c22df034fbffe691abe93cd80cea01d8 python-dateutil==2.8.2 ; python_version >= "3.10" and python_version < "3.12" \ --hash=sha256:0123cacc1627ae19ddf3c27a5de5bd67ee4586fbdd6440d9748f8abb483d3e86 \ --hash=sha256:961d03dc3453ebbc59dbdea9e4e11c5651520a876d0f4db161e8674aae935da9 @@ -2092,11 +2093,11 @@ scipy==1.11.4 ; python_version >= "3.10" and python_version < "3.12" \ --hash=sha256:ee410e6de8f88fd5cf6eadd73c135020bfbbbdfcd0f6162c36a7638a1ea8cc65 \ --hash=sha256:f313b39a7e94f296025e3cffc2c567618174c0b1dde173960cf23808f9fae4be \ --hash=sha256:f3cd9e7b3c2c1ec26364856f9fbe78695fe631150f94cd1c22228456404cf1ec -searvey==0.3.10 ; python_version >= "3.10" and python_version < "3.12" \ - --hash=sha256:f1a0e658cf9b67a120650d561b4ea09b3a939d29967c24d245883f08dd31cb3a -setuptools==69.0.2 ; python_version >= "3.10" and python_version < "3.12" \ - --hash=sha256:1e8fdff6797d3865f37397be788a4e3cba233608e9b509382a2777d25ebde7f2 \ - --hash=sha256:735896e78a4742605974de002ac60562d286fa8051a7e2299445e8e8fbb01aa6 +searvey==0.3.11 ; python_version >= "3.10" and python_version < "3.12" \ + --hash=sha256:ccb2199fe70c8ba8d92d84db7fe452163c156f8967c5d3c7ab646549975ff48b +setuptools==69.0.3 ; python_version >= "3.10" and python_version < "3.12" \ + --hash=sha256:385eb4edd9c9d5c17540511303e39a147ce2fc04bc55289c322b9e5904fe2c05 \ + --hash=sha256:be1af57fc409f93647f2e8e4573a142ed38724b8cdd389706a867bb4efcf1e78 shapely==2.0.2 ; python_version >= "3.10" and python_version < "3.12" \ --hash=sha256:03e63a99dfe6bd3beb8d5f41ec2086585bb969991d603f9aeac335ad396a06d4 \ --hash=sha256:0521d76d1e8af01e712db71da9096b484f081e539d4f4a8c97342e7971d5e1b4 \ @@ -2184,18 +2185,18 @@ tornado==6.4 ; python_version >= "3.10" and python_version < "3.12" \ tqdm==4.66.1 ; python_version >= "3.10" and python_version < "3.12" \ --hash=sha256:d302b3c5b53d47bce91fea46679d9c3c6508cf6332229aa1e7d8653723793386 \ --hash=sha256:d88e651f9db8d8551a62556d3cff9e3034274ca5d66e93197cf2490e2dcb69c7 -traitlets==5.14.0 ; python_version >= "3.10" and python_version < "3.12" \ - --hash=sha256:f14949d23829023013c47df20b4a76ccd1a85effb786dc060f34de7948361b33 \ - --hash=sha256:fcdaa8ac49c04dfa0ed3ee3384ef6dfdb5d6f3741502be247279407679296772 +traitlets==5.14.1 ; python_version >= "3.10" and python_version < "3.12" \ + --hash=sha256:2e5a030e6eff91737c643231bfcf04a65b0132078dad75e4936700b213652e74 \ + --hash=sha256:8585105b371a04b8316a43d5ce29c098575c2e477850b62b848b964f1444527e typer[all]==0.9.0 ; python_version >= "3.10" and python_version < "3.12" \ --hash=sha256:50922fd79aea2f4751a8e0408ff10d2662bd0c8bbfa84755a699f3bada2978b2 \ --hash=sha256:5d96d986a21493606a358cae4461bd8cdf83cbf33a5aa950ae629ca3b51467ee typing-extensions==4.9.0 ; python_version >= "3.10" and python_version < "3.12" \ --hash=sha256:23478f88c37f27d76ac8aee6c905017a143b0b1b886c3c9f66bc2fd94f9f5783 \ --hash=sha256:af72aea155e91adfc61c3ae9e0e342dbc0cba726d6cba4b6c72c1f34e47291cd -tzdata==2023.3 ; python_version >= "3.10" and python_version < "3.12" \ - --hash=sha256:11ef1e08e54acb0d4f95bdb1be05da659673de4acbd21bf9c69e94cc5e907a3a \ - --hash=sha256:7e65763eef3120314099b6939b5546db7adce1e7d6f2e179e3df563c70511eda +tzdata==2023.4 ; python_version >= "3.10" and python_version < "3.12" \ + --hash=sha256:aa3ace4329eeacda5b7beb7ea08ece826c28d761cda36e747cfbf97996d39bf3 \ + --hash=sha256:dd54c94f294765522c77399649b4fefd95522479a664a0cec87f41bebc6148c9 uc-micro-py==1.0.2 ; python_version >= "3.10" and python_version < "3.12" \ --hash=sha256:30ae2ac9c49f39ac6dce743bd187fcd2b574b16ca095fa74cd9396795c954c54 \ --hash=sha256:8c9110c309db9d9e87302e2f4ad2c3152770930d88ab385cd544e7a7e75f3de0 @@ -2205,9 +2206,9 @@ urllib3==2.1.0 ; python_version >= "3.10" and python_version < "3.12" \ utide==0.3.0 ; python_version >= "3.10" and python_version < "3.12" \ --hash=sha256:911a41fd8c838ae8d6a0b09e2570e8d849c10b4fc7a078da2810d9d1a9d22429 \ --hash=sha256:99f730b730a286501143561916c7c619ce4ca664bfc95ee9660ca7a5698d0ef3 -wcwidth==0.2.12 ; python_version >= "3.10" and python_version < "3.12" \ - --hash=sha256:f01c104efdf57971bcb756f054dd58ddec5204dd15fa31d6503ea57947d97c02 \ - --hash=sha256:f26ec43d96c8cbfed76a5075dac87680124fa84e0855195a6184da9c187f133c +wcwidth==0.2.13 ; python_version >= "3.10" and python_version < "3.12" \ + --hash=sha256:3da69048e4540d84af32131829ff948f1e022c1c6bdb8d6102117aac784f6859 \ + --hash=sha256:72ea0c06399eb286d978fdedb6923a9eb47e1c486ce63e9b4e64fc18303972b5 webencodings==0.5.1 ; python_version >= "3.10" and python_version < "3.12" \ --hash=sha256:a0af1213f3c2226497a97e2b3aa01a7e4bee4f403f95be16fc9acd2947514a78 \ --hash=sha256:b36a1c245f2d304965eb4e0a82848379241dc04b865afcc4aab16748587e1923 diff --git a/requirements/requirements.txt b/requirements/requirements.txt index a6aef58..80676b7 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -1,184 +1,62 @@ -adlfs==2023.10.0 ; python_version >= "3.10" and python_version < "3.12" \ - 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--hash=sha256:d3400f47dda0196b5af50b0b0678e33cc8c42e52e55ae0a63cdfed60725659bc \ + --hash=sha256:d3ae2bb5d4168912e438e377cc1301fa01df949ba59cd86317b3e00404fd4a97 \ + --hash=sha256:d41000ea7ca196b5fd39d6fccd34bf0704c8831731cedd2da2dcae3c6ac49c42 \ + --hash=sha256:f40724b6e965ff5b88b333d4a10097b1629e60c0db21bb3d08c24d7b1a904a16 \ + --hash=sha256:f4dfc22a3450227e692ef2ff4657639c33eec88ad04ee3ce29d1a23a4942da24 \ + --hash=sha256:fadfd2f3931fdff42f4b9867eb02ed7c662d01e6099ff6b347b6ced791450651 \ + --hash=sha256:fba5fd1805c71b2eeea50bea93d59be449c4af23ebd8da5f75fd74fd0331e314 +cartopy==0.22.0 ; python_version >= "3.9" and python_version < "3.12" \ --hash=sha256:319b8244f6d06f600be89ad5eaa85c2524e23b240decd203188da26ff14a346b \ --hash=sha256:3b25dd55844f36115e6c3632a16ed545cbee011068777b5a7a49ed3e1dbcafcb \ --hash=sha256:3d9d24ef50991269b57a42bdd4f1156426065fbeb41777c7e28d937e2c4a2ae6 \ @@ -195,63 +73,10 @@ cartopy==0.22.0 ; python_version >= "3.10" and python_version < "3.12" \ 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--hash=sha256:dc9b18bf40cc75f66f40a7379f6a9513244fe33c0e8aa72e2d56b0196a7ef872 \ - --hash=sha256:e09f3ff613345df5e8c3667da1d918f9149bd623cd9070c983c013792a9a62eb \ - --hash=sha256:e4108df7fe9b707191e55f33efbcb2d81928e10cea45527879a4749cbe472614 \ - --hash=sha256:e6024675e67af929088fda399b2094574609396b1decb609c55fa58b028a32a1 \ - --hash=sha256:e70f54f1796669ef691ca07d046cd81a29cb4deb1e5f942003f401c0c4a2695d \ - --hash=sha256:e715596e683d2ce000574bae5d07bd522c781a822866c20495e52520564f0969 \ - --hash=sha256:e760191dd42581e023a68b758769e2da259b5d52e3103c6060ddc02c9edb8d7b \ - --hash=sha256:ed86a35631f7bfbb28e108dd96773b9d5a6ce4811cf6ea468bb6a359b256b1e4 \ - --hash=sha256:ee07e47c12890ef248766a6e55bd38ebfb2bb8edd4142d56db91b21ea68b7627 \ - --hash=sha256:fa3a0128b152627161ce47201262d3140edb5a5c3da88d73a1b790a959126956 \ - --hash=sha256:fcc8eb6d5902bb1cf6dc4f187ee3ea80a1eba0a89aba40a5cb20a5087d961357 -cftime==1.6.3 ; python_version >= "3.10" and python_version < "3.12" \ +certifi==2024.2.2 ; python_version >= "3.9" and python_version < "3.12" \ + --hash=sha256:0569859f95fc761b18b45ef421b1290a0f65f147e92a1e5eb3e635f9a5e4e66f \ + --hash=sha256:dc383c07b76109f368f6106eee2b593b04a011ea4d55f652c6ca24a754d1cdd1 +cftime==1.6.3 ; python_version >= "3.9" and python_version < "3.12" \ --hash=sha256:022dabf1610cdd04a693e730fa8f71d307059717f29dba921e7486e553412bb4 \ --hash=sha256:0a38eb9f5c733a23e1714bd3ef2762ed5acee34f127670f8fb4ad6464946f6b3 \ --hash=sha256:2d113a01ab924445e61d65c26bbd95bc08e4a22878d3b947064bba056c884c4a \ @@ -273,7 +98,7 @@ cftime==1.6.3 ; python_version >= "3.10" and python_version < "3.12" \ --hash=sha256:d9b00c2844c7a1701d8ede5336b6321dfee256ceab81a34a1aff0483d56891a6 \ --hash=sha256:eb6dd70b2ccabfe1a14b7fbb0bbdce0418e71697094373c0d573c880790fa291 \ --hash=sha256:f9878bfd8c1c3f24184ecbd528f739ba46ebaceaf1c8a24d348d7befb117a285 -charset-normalizer==3.3.2 ; python_version >= "3.10" and python_version < "3.12" \ +charset-normalizer==3.3.2 ; python_version >= "3.9" and python_version < "3.12" \ --hash=sha256:06435b539f889b1f6f4ac1758871aae42dc3a8c0e24ac9e60c2384973ad73027 \ --hash=sha256:06a81e93cd441c56a9b65d8e1d043daeb97a3d0856d177d5c90ba85acb3db087 \ --hash=sha256:0a55554a2fa0d408816b3b5cedf0045f4b8e1a6065aec45849de2d6f3f8e9786 \ @@ -364,28 +189,22 @@ charset-normalizer==3.3.2 ; python_version >= "3.10" and python_version < "3.12" --hash=sha256:fb69256e180cb6c8a894fee62b3afebae785babc1ee98b81cdf68bbca1987f33 \ --hash=sha256:fd1abc0d89e30cc4e02e4064dc67fcc51bd941eb395c502aac3ec19fab46b519 \ --hash=sha256:ff8fa367d09b717b2a17a052544193ad76cd49979c805768879cb63d9ca50561 -click-plugins==1.1.1 ; python_version >= "3.10" and python_version < "3.12" \ +click-plugins==1.1.1 ; python_version >= "3.9" and python_version < "3.12" \ --hash=sha256:46ab999744a9d831159c3411bb0c79346d94a444df9a3a3742e9ed63645f264b \ --hash=sha256:5d262006d3222f5057fd81e1623d4443e41dcda5dc815c06b442aa3c02889fc8 -click==8.1.7 ; python_version >= "3.10" and python_version < "3.12" \ +click==8.1.7 ; python_version >= "3.9" and python_version < "3.12" \ --hash=sha256:ae74fb96c20a0277a1d615f1e4d73c8414f5a98db8b799a7931d1582f3390c28 \ --hash=sha256:ca9853ad459e787e2192211578cc907e7594e294c7ccc834310722b41b9ca6de -cligj==0.7.2 ; python_version >= "3.10" and python_version < "3.12" \ +cligj==0.7.2 ; python_version >= "3.9" and python_version < "3.12" \ --hash=sha256:a4bc13d623356b373c2c27c53dbd9c68cae5d526270bfa71f6c6fa69669c6b27 \ --hash=sha256:c1ca117dbce1fe20a5809dc96f01e1c2840f6dcc939b3ddbb1111bf330ba82df -cloudpickle==3.0.0 ; python_version >= "3.10" and python_version < "3.12" \ +cloudpickle==3.0.0 ; python_version >= "3.9" and python_version < "3.12" \ --hash=sha256:246ee7d0c295602a036e86369c77fecda4ab17b506496730f2f576d9016fd9c7 \ --hash=sha256:996d9a482c6fb4f33c1a35335cf8afd065d2a56e973270364840712d9131a882 -colorama==0.4.6 ; python_version >= "3.10" and python_version < "3.12" \ +colorama==0.4.6 ; python_version >= "3.9" and python_version < "3.12" and (sys_platform == "win32" or platform_system == "Windows") \ --hash=sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44 \ --hash=sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6 -colorcet==3.0.1 ; python_version >= "3.10" and python_version < "3.12" \ - --hash=sha256:51455a20353d12fac91f953772d8409f2474e6a0db1af3fa4f7005f405a2480b \ - --hash=sha256:8daff01824ee9935fdf762d15c444a67d3e361ad4f8b738ad59ac9bf38f30600 -comm==0.2.0 ; python_version >= "3.10" and python_version < "3.12" \ - --hash=sha256:2da8d9ebb8dd7bfc247adaff99f24dce705638a8042b85cb995066793e391001 \ - --hash=sha256:a517ea2ca28931c7007a7a99c562a0fa5883cfb48963140cf642c41c948498be -contourpy==1.2.0 ; python_version >= "3.10" and python_version < "3.12" \ +contourpy==1.2.0 ; python_version >= "3.9" and python_version < "3.12" \ --hash=sha256:0274c1cb63625972c0c007ab14dd9ba9e199c36ae1a231ce45d725cbcbfd10a8 \ 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--hash=sha256:cf60138ccc8004f117ab2a2bad513cc4d122e55864b4fe7adf4db20ca68a078f \ + --hash=sha256:d7e7e0993d0758933b1a241a432b42c2db22dfa37d4108342ab4afb9557cbe3e \ + --hash=sha256:e7b49ab49a3bea17802df6872f8d44f664ba8f9be0632a60c99b20b6db2165b7 \ + --hash=sha256:e9764df0e8778f06414b9d281a75235c1e85071f64bb5d71564b97c1306a2afc \ + --hash=sha256:ef6c1025a570354297d6c15f7d0f296d95f88bd3850066b7f1e7b4f2f4c13a39 \ + --hash=sha256:f386cf162b059809ecfac3bcc491a9ea17da69fa35c8ded8ad154cd4b933d5ec \ + --hash=sha256:fa93695d5c08544f4a0dfd0965f378e7afc410d8672816aff1e81be1f45dbf2e +multifutures==0.3.0 ; python_version >= "3.9" and python_version < "3.12" \ + --hash=sha256:282435e930b3c438931bae034917d107379db1ae522dd09e1b129358dd100606 \ + --hash=sha256:2fa077891d40968d558116c62ec0038582e05370b2e10a763acd4fa0d9cf4aa7 +netcdf4==1.6.5 ; python_version >= "3.9" and python_version < "3.12" \ --hash=sha256:0187646e3348e7a8cd654617dda65517df138042c94c2fcc6682ff7c8c6654dc \ --hash=sha256:081e9043ac6160989f60570928eabe803c88ce7df1d3f79f2345dc48f68ef752 \ --hash=sha256:10d2ac9ae1308ca837d86c6dc304ec455a85bdba0f2175e222844a54589168dc \ @@ -1321,35 +781,35 @@ netcdf4==1.6.5 ; python_version >= "3.10" and python_version < "3.12" \ --hash=sha256:b994afce2ca4073f6b757385a6c0ffec25ecaae2b8821535b303c7cdbf6de42b \ --hash=sha256:d23b97cbde2bf413fadc4697c5c255a0436511c02f811e127e0fb12f5b882a4c \ --hash=sha256:de4dc973fae9e2bbdf42e094125e423a4c25393172a61958314969b055a38889 -networkx==3.2.1 ; python_version >= "3.10" and python_version < "3.12" \ +networkx==3.2.1 ; python_version >= "3.9" and python_version < "3.12" \ --hash=sha256:9f1bb5cf3409bf324e0a722c20bdb4c20ee39bf1c30ce8ae499c8502b0b5e0c6 \ --hash=sha256:f18c69adc97877c42332c170849c96cefa91881c99a7cb3e95b7c659ebdc1ec2 -numba==0.58.1 ; python_version >= "3.10" and python_version < "3.12" \ - --hash=sha256:07f2fa7e7144aa6f275f27260e73ce0d808d3c62b30cff8906ad1dec12d87bbe \ - --hash=sha256:240e7a1ae80eb6b14061dc91263b99dc8d6af9ea45d310751b780888097c1aaa \ - --hash=sha256:45698b995914003f890ad839cfc909eeb9c74921849c712a05405d1a79c50f68 \ - --hash=sha256:487ded0633efccd9ca3a46364b40006dbdaca0f95e99b8b83e778d1195ebcbaa \ - --hash=sha256:4e79b6cc0d2bf064a955934a2e02bf676bc7995ab2db929dbbc62e4c16551be6 \ - --hash=sha256:55a01e1881120e86d54efdff1be08381886fe9f04fc3006af309c602a72bc44d \ - --hash=sha256:5c765aef472a9406a97ea9782116335ad4f9ef5c9f93fc05fd44aab0db486954 \ - --hash=sha256:6fe7a9d8e3bd996fbe5eac0683227ccef26cba98dae6e5cee2c1894d4b9f16c1 \ - --hash=sha256:7bf1ddd4f7b9c2306de0384bf3854cac3edd7b4d8dffae2ec1b925e4c436233f \ - --hash=sha256:811305d5dc40ae43c3ace5b192c670c358a89a4d2ae4f86d1665003798ea7a1a \ - --hash=sha256:81fe5b51532478149b5081311b0fd4206959174e660c372b94ed5364cfb37c82 \ - --hash=sha256:898af055b03f09d33a587e9425500e5be84fc90cd2f80b3fb71c6a4a17a7e354 \ - --hash=sha256:9e9356e943617f5e35a74bf56ff6e7cc83e6b1865d5e13cee535d79bf2cae954 \ - --hash=sha256:a1eaa744f518bbd60e1f7ccddfb8002b3d06bd865b94a5d7eac25028efe0e0ff \ - --hash=sha256:bc2d904d0319d7a5857bd65062340bed627f5bfe9ae4a495aef342f072880d50 \ - --hash=sha256:bcecd3fb9df36554b342140a4d77d938a549be635d64caf8bd9ef6c47a47f8aa \ - --hash=sha256:bd3dda77955be03ff366eebbfdb39919ce7c2620d86c906203bed92124989032 \ - --hash=sha256:bf68df9c307fb0aa81cacd33faccd6e419496fdc621e83f1efce35cdc5e79cac \ - --hash=sha256:d3e2fe81fe9a59fcd99cc572002101119059d64d31eb6324995ee8b0f144a306 \ - --hash=sha256:e63d6aacaae1ba4ef3695f1c2122b30fa3d8ba039c8f517784668075856d79e2 \ - --hash=sha256:ea5bfcf7d641d351c6a80e8e1826eb4a145d619870016eeaf20bbd71ef5caa22 -numbagg==0.6.7 ; python_version >= "3.10" and python_version < "3.12" \ - --hash=sha256:c978e34e9e68775cf7bdd2380e0a93fd27213df0c35d71be180bd47c77a64fc6 \ - --hash=sha256:fc70b14367254a99bfa279d2ce17aa9fa9ccd5982458f605d0d2b50045c60219 -numcodecs==0.12.1 ; python_version >= "3.10" and python_version < "3.12" \ +numba==0.59.0 ; python_version >= "3.9" and python_version < "3.12" \ + --hash=sha256:0307ee91b24500bb7e64d8a109848baf3a3905df48ce142b8ac60aaa406a0400 \ + --hash=sha256:1192d6b2906bf3ff72b1d97458724d98860ab86a91abdd4cfd9328432b661e31 \ + --hash=sha256:12b9b064a3e4ad00e2371fc5212ef0396c80f41caec9b5ec391c8b04b6eaf2a8 \ + --hash=sha256:32bd0a41525ec0b1b853da244808f4e5333867df3c43c30c33f89cf20b9c2b63 \ + --hash=sha256:4981659220b61a03c1e557654027d271f56f3087448967a55c79a0e5f926de62 \ + --hash=sha256:5516a469514bfae52a9d7989db4940653a5cbfac106f44cb9c50133b7ad6224b \ + --hash=sha256:6feb1504bb432280f900deaf4b1dadcee68812209500ed3f81c375cbceab24dc \ + --hash=sha256:70d26ba589f764be45ea8c272caa467dbe882b9676f6749fe6f42678091f5f21 \ + --hash=sha256:753dc601a159861808cc3207bad5c17724d3b69552fd22768fddbf302a817a4c \ + --hash=sha256:8cbef55b73741b5eea2dbaf1b0590b14977ca95a13a07d200b794f8f6833a01c \ + --hash=sha256:8d061d800473fb8fef76a455221f4ad649a53f5e0f96e3f6c8b8553ee6fa98fa \ + --hash=sha256:90efb436d3413809fcd15298c6d395cb7d98184350472588356ccf19db9e37c8 \ + --hash=sha256:944faad25ee23ea9dda582bfb0189fb9f4fc232359a80ab2a028b94c14ce2b1d \ + --hash=sha256:9e20736bf62e61f8353fb71b0d3a1efba636c7a303d511600fc57648b55823ed \ + --hash=sha256:c086a434e7d3891ce5dfd3d1e7ee8102ac1e733962098578b507864120559ceb \ + --hash=sha256:cd3dac45e25d927dcb65d44fb3a973994f5add2b15add13337844afe669dd1ba \ + --hash=sha256:ce62bc0e6dd5264e7ff7f34f41786889fa81a6b860662f824aa7532537a7bee0 \ + --hash=sha256:d540f69a8245fb714419c2209e9af6104e568eb97623adc8943642e61f5d6d8e \ + --hash=sha256:e125f7d69968118c28ec0eed9fbedd75440e64214b8d2eac033c22c04db48492 \ + --hash=sha256:e86e6786aec31d2002122199486e10bbc0dc40f78d76364cded375912b13614c \ + --hash=sha256:fe4d7562d1eed754a7511ed7ba962067f198f86909741c5c6e18c4f1819b1f47 +numbagg==0.8.1 ; python_version >= "3.9" and python_version < "3.12" \ + --hash=sha256:3a111361e6062feafd600b1e3c8a8187fd55e955bee735ada843705395fe84d9 \ + --hash=sha256:fc889d7b890bf5e729e64add34852bc70b1447eb53a29fca8b32d345ed792a65 +numcodecs==0.12.1 ; python_version >= "3.9" and python_version < "3.12" \ --hash=sha256:05d91a433733e7eef268d7e80ec226a0232da244289614a8f3826901aec1098e \ --hash=sha256:0e79bf9d1d37199ac00a60ff3adb64757523291d19d03116832e600cac391c51 \ --hash=sha256:135b2d47563f7b9dc5ee6ce3d1b81b0f1397f69309e909f1a35bb0f7c553d45e \ @@ -1371,337 +831,293 @@ numcodecs==0.12.1 ; python_version >= "3.10" and python_version < "3.12" \ --hash=sha256:eeaf42768910f1c6eebf6c1bb00160728e62c9343df9e2e315dc9fe12e3f6071 \ --hash=sha256:ef964d4860d3e6b38df0633caf3e51dc850a6293fd8e93240473642681d95136 \ --hash=sha256:f2207871868b2464dc11c513965fd99b958a9d7cde2629be7b2dc84fdaab013b -numpy-groupies==0.10.2 ; python_version >= "3.10" and python_version < "3.12" \ +numpy-groupies==0.10.2 ; python_version >= "3.9" and python_version < "3.12" \ --hash=sha256:24a574ecdd58c0d669749b67ec31dab971d27e04037b402bbc4f6bedaa2623bc \ 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--hash=sha256:17c46087b53c6aec9bc5495a5d52aca19b2aa4b045f370b9f4b3a6058f5d8df7 \ + --hash=sha256:a24cc3bc8c78089542699382bf650d57a055c82bb72c342fa7caa6618b9e4417 +pyparsing==3.1.2 ; python_version >= "3.9" and python_version < "3.12" \ + --hash=sha256:a1bac0ce561155ecc3ed78ca94d3c9378656ad4c94c1270de543f621420f94ad \ + --hash=sha256:f9db75911801ed778fe61bb643079ff86601aca99fcae6345aa67292038fb742 +pyproj==3.6.1 ; python_version >= "3.9" and python_version < "3.12" \ --hash=sha256:18faa54a3ca475bfe6255156f2f2874e9a1c8917b0004eee9f664b86ccc513d3 \ --hash=sha256:1e9fbaf920f0f9b4ee62aab832be3ae3968f33f24e2e3f7fbb8c6728ef1d9746 \ --hash=sha256:2d6ff73cc6dbbce3766b6c0bce70ce070193105d8de17aa2470009463682a8eb \ @@ -1729,37 +1145,19 @@ pyproj==3.6.1 ; python_version >= "3.10" and python_version < "3.12" \ --hash=sha256:fd43bd9a9b9239805f406fd82ba6b106bf4838d9ef37c167d3ed70383943ade1 \ --hash=sha256:fd93c1a0c6c4aedc77c0fe275a9f2aba4d59b8acf88cebfc19fe3c430cfabf4f \ --hash=sha256:fffb059ba3bced6f6725961ba758649261d85ed6ce670d3e3b0a26e81cf1aa8d -pyshp==2.3.1 ; python_version >= "3.10" and python_version < "3.12" \ +pyshp==2.3.1 ; python_version >= "3.9" and python_version < "3.12" \ --hash=sha256:4caec82fd8dd096feba8217858068bacb2a3b5950f43c048c6dc32a3489d5af1 \ --hash=sha256:67024c0ccdc352ba5db777c4e968483782dfa78f8e200672a90d2d30fd8b7b49 -python-dateutil==2.8.2 ; python_version >= "3.10" and python_version < "3.12" \ - --hash=sha256:0123cacc1627ae19ddf3c27a5de5bd67ee4586fbdd6440d9748f8abb483d3e86 \ - --hash=sha256:961d03dc3453ebbc59dbdea9e4e11c5651520a876d0f4db161e8674aae935da9 -python-dotenv==1.0.0 ; python_version >= "3.10" and python_version < "3.12" \ - --hash=sha256:a8df96034aae6d2d50a4ebe8216326c61c3eb64836776504fcca410e5937a3ba \ - --hash=sha256:f5971a9226b701070a4bf2c38c89e5a3f0d64de8debda981d1db98583009122a -pytz==2023.3.post1 ; python_version >= "3.10" and python_version < "3.12" \ - --hash=sha256:7b4fddbeb94a1eba4b557da24f19fdf9db575192544270a9101d8509f9f43d7b \ - --hash=sha256:ce42d816b81b68506614c11e8937d3aa9e41007ceb50bfdcb0749b921bf646c7 -pyviz-comms==3.0.0 ; python_version >= "3.10" and python_version < "3.12" \ - --hash=sha256:91c967151b1e4d436c458c147a31991a42fbe7567e49176e4eb5b8dc8e20f1ff \ - --hash=sha256:f4ca91e4157a64e3abed7cc249e60b9a8d2532f8832f1cb075914d19337d2ba6 -pywin32==306 ; python_version >= "3.10" and python_version < "3.12" and platform_system == "Windows" \ - --hash=sha256:06d3420a5155ba65f0b72f2699b5bacf3109f36acbe8923765c22938a69dfc8d \ - --hash=sha256:1c73ea9a0d2283d889001998059f5eaaba3b6238f767c9cf2833b13e6a685f65 \ - --hash=sha256:37257794c1ad39ee9be652da0462dc2e394c8159dfd913a8a4e8eb6fd346da0e \ - --hash=sha256:383229d515657f4e3ed1343da8be101000562bf514591ff383ae940cad65458b \ - --hash=sha256:39b61c15272833b5c329a2989999dcae836b1eed650252ab1b7bfbe1d59f30f4 \ - --hash=sha256:5821ec52f6d321aa59e2db7e0a35b997de60c201943557d108af9d4ae1ec7040 \ - --hash=sha256:70dba0c913d19f942a2db25217d9a1b726c278f483a919f1abfed79c9cf64d3a \ - --hash=sha256:72c5f621542d7bdd4fdb716227be0dd3f8565c11b280be6315b06ace35487d36 \ - --hash=sha256:84f4471dbca1887ea3803d8848a1616429ac94a4a8d05f4bc9c5dcfd42ca99c8 \ - --hash=sha256:a7639f51c184c0272e93f244eb24dafca9b1855707d94c192d4a0b4c01e1100e \ - --hash=sha256:e25fd5b485b55ac9c057f67d94bc203f3f6595078d1fb3b458c9c28b7153a802 \ - --hash=sha256:e4c092e2589b5cf0d365849e73e02c391c1349958c5ac3e9d5ccb9a28e017b3a \ - --hash=sha256:e65028133d15b64d2ed8f06dd9fbc268352478d4f9289e69c190ecd6818b6407 \ - --hash=sha256:e8ac1ae3601bee6ca9f7cb4b5363bf1c0badb935ef243c4733ff9a393b1690c0 -pyyaml==6.0.1 ; python_version >= "3.10" and python_version < "3.12" \ +pytest==8.0.2 ; python_version >= "3.9" and python_version < "3.12" \ + --hash=sha256:d4051d623a2e0b7e51960ba963193b09ce6daeb9759a451844a21e4ddedfc1bd \ + --hash=sha256:edfaaef32ce5172d5466b5127b42e0d6d35ebbe4453f0e3505d96afd93f6b096 +python-dateutil==2.9.0.post0 ; python_version >= "3.9" and python_version < "3.12" \ + --hash=sha256:37dd54208da7e1cd875388217d5e00ebd4179249f90fb72437e91a35459a0ad3 \ + --hash=sha256:a8b2bc7bffae282281c8140a97d3aa9c14da0b136dfe83f850eea9a5f7470427 +pytz==2024.1 ; python_version >= "3.9" and python_version < "3.12" \ + --hash=sha256:2a29735ea9c18baf14b448846bde5a48030ed267578472d8955cd0e7443a9812 \ + --hash=sha256:328171f4e3623139da4983451950b28e95ac706e13f3f2630a879749e7a8b319 +pyyaml==6.0.1 ; python_version >= "3.9" and python_version < "3.12" \ --hash=sha256:04ac92ad1925b2cff1db0cfebffb6ffc43457495c9b3c39d3fcae417d7125dc5 \ --hash=sha256:062582fca9fabdd2c8b54a3ef1c978d786e0f6b3a1510e0ac93ef59e0ddae2bc \ --hash=sha256:0d3304d8c0adc42be59c5f8a4d9e3d7379e6955ad754aa9d6ab7a398b59dd1df \ @@ -1810,13 +1208,10 @@ pyyaml==6.0.1 ; python_version >= "3.10" and python_version < "3.12" \ 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--hash=sha256:1a5db485fe2de4403f13fafdc231b0dbae5eca4359232d2efc79025527375b09 \ @@ -2088,112 +1449,15 @@ wrapt==1.16.0 ; python_version >= "3.10" and python_version < "3.12" \ --hash=sha256:f6b2d0c6703c988d334f297aa5df18c45e97b0af3679bb75059e0e0bd8b1069d \ --hash=sha256:f8212564d49c50eb4565e502814f694e240c55551a5f1bc841d4fcaabb0a9b8a \ --hash=sha256:ffa565331890b90056c01db69c0fe634a776f8019c143a5ae265f9c6bc4bd6d4 -xarray==2023.12.0 ; python_version >= "3.10" and python_version < "3.12" \ - --hash=sha256:3c22b6824681762b6c3fcad86dfd18960a617bccbc7f456ce21b43a20e455fb9 \ - --hash=sha256:4565dbc890de47e278346c44d6b33bb07d3427383e077a7ca8ab6606196fd433 -xarray[accel,io,parallel]==2023.12.0 ; python_version >= "3.10" and python_version < "3.12" \ - --hash=sha256:3c22b6824681762b6c3fcad86dfd18960a617bccbc7f456ce21b43a20e455fb9 \ - --hash=sha256:4565dbc890de47e278346c44d6b33bb07d3427383e077a7ca8ab6606196fd433 -xarray[accel,io]==2023.12.0 ; python_version >= "3.10" and python_version < "3.12" 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b/scripts/detide_gesla.py deleted file mode 100644 index 83d5bc9..0000000 --- a/scripts/detide_gesla.py +++ /dev/null @@ -1,130 +0,0 @@ -import os -import sys - -import numpy as np -import pandas as pd - -from analysea.filters import filter_fft -from analysea.filters import remove_numerical -from analysea.filters import signaltonoise -from analysea.gesla import GeslaDataset -from analysea.plot import plot_gaps -from analysea.plot import plot_multiyear_tide_analysis -from analysea.spikes import despike_prominence -from analysea.steps import step_function_ruptures -from analysea.tide import yearly_tide_analysis -from analysea.utils import correct_unit -from analysea.utils import detect_gaps - -# =================== -# global variables -# =================== -# load in data information -# GESLA_CODE = "acajutla-082c-slv-uhslc" -GESLA_CODE = "a2-a2-bel-cmems" - -# tidal analysis options -OPTS = { - "conf_int": "linear", - "constit": "auto", - "method": "ols", # ols is faster and good for missing data (Ponchaut et al., 2001) - "order_constit": "frequency", - "Rayleigh_min": 0.97, - "lat": None, - "verbose": True, -} # careful if there is only one Nan parameter, the analysis crashes - -ASTRO_PLOT = [ - "M2", - "S2", - "N2", - "O1", - "K2", - "K1", - "NU2", - "Q1", - "L2", - "P1", - "2N2", - "M4", - "MS4", - "MM", - "MU2", - "SSA", - "LDA2", - "MF", - "MSM", - "MN4", -] - -ASTRO_WRITE = [ - "M2", - "S2", - "N2", - "O1", - "K2", - "K1", - "NU2", - "Q1", - "L2", - "P1", - "2N2", - "M4", - "MS4", - "MM", - "MU2", - "SSA", - "LDA2", - "MF", - "MSM", - "MN4", -] - -meta_file = "/home/tomsail/apps/obs/GESLA/GESLA3_ALL.csv" -data_path = "/home/tomsail/apps/obs/GESLA/GESLA3.0_ALL/" - -g3 = GeslaDataset(meta_file=meta_file, data_path=data_path) - - -# main call -def main(): - # fmt: off - _gesla_index = np.where(g3.meta.filename == GESLA_CODE)[0][0] - df0, meta = g3.file_to_pandas(GESLA_CODE) - # - lat = g3.meta.iloc[_gesla_index].latitude - lon = g3.meta.iloc[_gesla_index].longitude - title = g3.meta.iloc[_gesla_index].site_name - filenameOutGaps = os.path.join('tests/data/graphs', GESLA_CODE + "_gaps.png") - filenameOut = os.path.join('tests/data/graphs', GESLA_CODE + ".png") - # - df = pd.DataFrame() - df["slevel"] = remove_numerical(df0.slevel) - df["correct"], _ = correct_unit(df.slevel) - df["step"], stepsx, steps = step_function_ruptures(df.correct) - if (len(stepsx) > 2) and (np.max(steps) > 1.0): - # if step are not bigger than 1 meter - df.correct = df.slevel - df.step - threshold = np.max([1,df.correct.std() * 3]) - _, _, df["anomaly"] = despike_prominence(df.correct, threshold) - _, _, big_gaps = detect_gaps(df) - plot_gaps(df.anomaly, big_gaps, filenameOutGaps) - # assign parameters for tide analysis - if signaltonoise(df.slevel) < 0: - df["filtered"] = filter_fft(df.anomaly) - df.anomaly = df.filtered - # - OPTS["lat"] = lat - df["tide"], df["surge"], coefs, years = yearly_tide_analysis(df.anomaly, 365, OPTS) - plot_multiyear_tide_analysis(ASTRO_PLOT, coefs, years, lat, lon, df, title, filenameOut) - plot_multiyear_tide_analysis(ASTRO_PLOT, coefs, years, lat, lon, df, title, filenameOut, zoom=True) - # fmt: on - - # <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<< - # ~~~~ Jenkins' success message ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - print("\n\nMy work is done\n\n") - sys.exit(0) - - -if __name__ == "__main__": - main() diff --git a/scripts/detide_gesla_all.py b/scripts/detide_gesla_all.py deleted file mode 100644 index 566478e..0000000 --- a/scripts/detide_gesla_all.py +++ /dev/null @@ -1,219 +0,0 @@ -import json -import os -import sys - -import numpy as np -import pandas as pd - -from analysea.filters import filter_fft -from analysea.filters import remove_numerical -from analysea.filters import signaltonoise -from analysea.gesla import GeslaDataset -from analysea.plot import plot_gaps -from analysea.plot import plot_multiyear_tide_analysis -from analysea.spikes import despike_prominence -from analysea.steps import step_function_ruptures -from analysea.tide import demean_amps_phases -from analysea.tide import yearly_tide_analysis -from analysea.utils import completeness -from analysea.utils import correct_unit -from analysea.utils import detect_gaps - -# =================== -# global variables -# =================== -# tidal analysis options -OPTS = { - "conf_int": "linear", - "constit": "auto", - "method": "ols", # ols is faster and good for missing data (Ponchaut et al., 2001) - "order_constit": "frequency", - "Rayleigh_min": 0.97, - "lat": None, - "verbose": False, -} # careful if there is only one Nan parameter, the analysis crashes - -ASTRO_PLOT = [ - "M2", - "S2", - "N2", - "O1", - "K2", - "K1", - "NU2", - "Q1", - "L2", - "P1", - "2N2", - "M4", - "MS4", - "MM", - "MU2", - "SSA", - "LDA2", - "MF", - "MSM", - "MN4", -] - -ASTRO_WRITE = [ - "M2", - "S2", - "N2", - "O1", - "K2", - "K1", - "NU2", - "Q1", - "L2", - "P1", - "2N2", - "M4", - "MS4", - "MM", - "MU2", - "SSA", - "LDA2", - "MF", - "MSM", - "MN4", -] - -meta_file = "/home/tomsail/apps/obs/GESLA/GESLA3_ALL.csv" -data_path = "/home/tomsail/apps/obs/GESLA/GESLA3.0_ALL/" - -g3 = GeslaDataset(meta_file=meta_file, data_path=data_path) -GESLA_STATIONS = pd.DataFrame(g3.meta) -GESLA_STATIONS["first_obs"] = np.empty(len(GESLA_STATIONS), dtype=str) -GESLA_STATIONS["last_obs"] = np.empty(len(GESLA_STATIONS), dtype=str) -GESLA_STATIONS["missing"] = np.empty(len(GESLA_STATIONS)) -GESLA_STATIONS["biggest_gap"] = np.empty(len(GESLA_STATIONS), dtype=str) -GESLA_STATIONS["total_gaps"] = np.empty(len(GESLA_STATIONS)) -GESLA_STATIONS["steps"] = np.empty(len(GESLA_STATIONS)) -GESLA_STATIONS["snr"] = np.empty(len(GESLA_STATIONS)) -GESLA_STATIONS["unit_flag"] = np.empty(len(GESLA_STATIONS), dtype=bool) -GESLA_STATIONS["perc_analysed"] = np.empty(len(GESLA_STATIONS)) -GESLA_STATIONS["analysis"] = np.empty(len(GESLA_STATIONS), dtype=str) -GESLA_STATIONS["analysed"] = np.empty(len(GESLA_STATIONS), dtype=bool) - - -# main call -def main(): - for _gesla_file in sorted(g3.meta.filename): - # here is the IOC case - _gesla_index = np.where(g3.meta.filename == _gesla_file)[0][0] - df0, meta = g3.file_to_pandas(_gesla_file) - df0 = df0.where(df0.use_flag == 1) - print(" > _gesla_file = ", _gesla_file, "-", len(df0), "records") - # fill already some info - lat = GESLA_STATIONS.iloc[_gesla_index].latitude - lon = GESLA_STATIONS.iloc[_gesla_index].longitude - title = GESLA_STATIONS.iloc[_gesla_index].site_name - filenameOut = os.path.join("./tests/data/graphs", _gesla_file + ".png") - filenameOutGaps = os.path.join("./tests/data/graphs", _gesla_file + "_gaps.png") - OPTS["lat"] = lat - # check if the file exists - if os.path.exists(filenameOut): - print(" > file ", filenameOut, " already exists") - continue - # - try: - df = pd.DataFrame() - df["slevel"] = remove_numerical(df0.slevel) - df["correct"], units_flag = correct_unit(df.slevel) - df["step"], stepsx, steps = step_function_ruptures(df.correct) - threshold = np.max([1, df.correct.std() * 3]) - if (len(stepsx) > 2) and (np.max(steps) > 1.0): # if step are not bigger than 1 meter - ipeaks, peaks, df["correct"] = despike_prominence( - df.correct - df.step, threshold - ) # despike once already - _, _, df["anomaly"] = despike_prominence(df.correct, threshold) - # calculate completeness - if completeness(df.anomaly) < 70: - GESLA_STATIONS.loc[_gesla_index, ["analysis"]] = "Failed: missing data" - GESLA_STATIONS.loc[_gesla_index, ["missing"]] = 100 - np.round(completeness(df.anomaly), 2) - GESLA_STATIONS.loc[_gesla_index, ["analysed"]] = False - print("completeness < 70% :", completeness(df.anomaly)) - continue - # - min_time = pd.Timestamp(df.index.min()) - max_time = pd.Timestamp(df.index.max()) - if (max_time - min_time).days < 365: - GESLA_STATIONS.loc[_gesla_index, ["analysis"]] = "Failed: time span less than a year" - GESLA_STATIONS.loc[_gesla_index, ["missing"]] = 100 - np.round(completeness(df.anomaly), 2) - GESLA_STATIONS.loc[_gesla_index, ["analysed"]] = False - print( - "ignore : period is less than a year:", - (max_time - min_time).days, - "days", - ) - continue - else: - # assign parameters for tide analysis - if signaltonoise(df.slevel) < 0: - df["filtered"] = filter_fft(df.anomaly) - df.anomaly = df.filtered - # detect big gaps - gaps, small_gaps, big_gaps = detect_gaps(df) - plot_gaps(df.anomaly, big_gaps, filenameOutGaps) - # assign parameters before running tides (eventual crash) - GESLA_STATIONS.loc[_gesla_index, ["missing"]] = 100 - np.round(completeness(df.anomaly), 2) - GESLA_STATIONS.loc[_gesla_index, ["biggest_gap"]] = str(big_gaps.max()) - GESLA_STATIONS.loc[_gesla_index, ["total_gaps"]] = len(gaps) - GESLA_STATIONS.loc[_gesla_index, ["steps"]] = len(stepsx) - GESLA_STATIONS.loc[_gesla_index, ["snr"]] = (signaltonoise(df.slevel),) - GESLA_STATIONS.loc[_gesla_index, ["unit_flag"]] = units_flag - # - df["tide"], df["surge"], coefs, years = yearly_tide_analysis(df.anomaly, 365, OPTS) - # save figures - if len(coefs) == 0: - GESLA_STATIONS.iloc[_gesla_index]["analysis"] = "Failed: missing data" - continue - # printing out results - GESLA_STATIONS.loc[_gesla_index, ["perc_analysed"]] = len(df.surge) / len(df.slevel) * 100 - GESLA_STATIONS.loc[_gesla_index, ["analysis"]] = "Success" - GESLA_STATIONS.loc[_gesla_index, ["analysed"]] = True - with open(f"./data/processed/{_gesla_file}.json", "w") as fp: - json.dump( - { - "const": ASTRO_WRITE, - "amps": demean_amps_phases(coefs, ASTRO_WRITE)[0].tolist(), - "phases": demean_amps_phases(coefs, ASTRO_WRITE)[1].tolist(), - }, - fp, - ) - out = pd.DataFrame(data=df.surge, index=df.index) - out.to_parquet(f"./data/processed/{_gesla_file}_surge.gzip", compression="gzip") - # plots - plot_multiyear_tide_analysis(ASTRO_PLOT, coefs, years, lat, lon, df, title, filenameOut) - plot_multiyear_tide_analysis( - ASTRO_PLOT, - coefs, - years, - lat, - lon, - df, - title, - filenameOut, - zoom=True, - ) - - except Exception as e: - print("Error: ", e) - GESLA_STATIONS.loc[_gesla_index, ["first_obs"]] = ( - pd.Timestamp(df0.index.min()).strftime("%Y-%m-%dT%H:%M:%S"), - ) - GESLA_STATIONS.loc[_gesla_index, ["last_obs"]] = ( - pd.Timestamp(df0.index.max()).strftime("%Y-%m-%dT%H:%M:%S"), - ) - GESLA_STATIONS.loc[_gesla_index, ["analysed"]] = False - GESLA_STATIONS.to_parquet("GESLA_STATIONS_api_analysed.gzip") - - # <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<< - # ~~~~ Jenkins' success message ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - print("\n\nMy work is done\n\n") - sys.exit(0) - - -if __name__ == "__main__": - main() diff --git a/scripts/detide_ioc.py b/scripts/detide_ioc.py deleted file mode 100644 index eb32af4..0000000 --- a/scripts/detide_ioc.py +++ /dev/null @@ -1,146 +0,0 @@ -#!/usr/bin/env python -import json -import os -import sys - -import numpy as np -import pandas as pd -from searvey import ioc - -from analysea.filters import remove_numerical -from analysea.plot import plot_gaps -from analysea.plot import plot_multiyear_tide_analysis -from analysea.spikes import despike_prominence -from analysea.steps import step_function_ruptures -from analysea.tide import demean_amps_phases -from analysea.tide import yearly_tide_analysis -from analysea.utils import correct_unit -from analysea.utils import detect_gaps -from analysea.utils import json_format - -# =================== -# global variables -# =================== -# load in data information -IOC_STATIONS = ioc.get_ioc_stations() -IOC_CODE = "acap2" - -# tidal analysis options -OPTS = { - "conf_int": "linear", - "constit": "auto", - "method": "ols", # ols is faster and good for missing data (Ponchaut et al., 2001) - "order_constit": "frequency", - "Rayleigh_min": 0.97, - "lat": None, - "verbose": False, -} # careful if there is only one Nan parameter, the analysis crashes - -ASTRO_PLOT = [ - "M2", - "S2", - "N2", - "O1", - "K2", - "K1", - "NU2", - "Q1", - "L2", - "P1", - "2N2", - "M4", - "MS4", - "MM", - "MU2", - "SSA", - "LDA2", - "MF", - "MSM", - "MN4", -] - -ASTRO_WRITE = [ - "M2", - "S2", - "N2", - "O1", - "K2", - "K1", - "NU2", - "Q1", - "L2", - "P1", - "2N2", - "M4", - "MS4", - "MM", - "MU2", - "SSA", - "LDA2", - "MF", - "MSM", - "MN4", -] - - -# main call -def main(): - # fmt: off - _ioc_index = np.where(IOC_STATIONS.ioc_code == IOC_CODE)[0][0] - df0 = pd.read_parquet(os.path.join( "tests/data", IOC_CODE + ".gzip")) - # - lat = IOC_STATIONS.iloc[_ioc_index].lat - lon = IOC_STATIONS.iloc[_ioc_index].lon - title = IOC_STATIONS.iloc[_ioc_index].location - OPTS["lat"] = lat - - # start completing the json export file - js_1 = dict() - js_1['lat'] = lat - js_1['lon'] = lon - min_time = pd.Timestamp(df0.index.min()) - max_time = pd.Timestamp(df0.index.max()) - js_1['first_obs'] = min_time - js_1['last_obs'] = max_time - js_1 = json_format(js_1) - - filenameOutGaps = os.path.join('tests/data/graphs', IOC_CODE + "_gaps.png") - filenameOut = os.path.join('tests/data/graphs', IOC_CODE + ".png") - # - df = pd.DataFrame() - df["slevel"] = remove_numerical(df0.slevel) - df["correct"], _ = correct_unit(df.slevel) - df["step"], stepsx, steps = step_function_ruptures(df.correct) - threshold = np.max([1,df.correct.std() * 3]) - if (len(stepsx) > 2) and (np.max(steps) > 1.0): - ipeaks, peaks, df['correct'] = despike_prominence(df.correct - df.step, threshold) # despike once already - _, _, df["anomaly"] = despike_prominence(df.correct, threshold) - # detect big gaps - _, _, big_gaps = detect_gaps(df) - plot_gaps(df.anomaly, big_gaps, filenameOutGaps) - # - df1 = df.reset_index().drop_duplicates(subset="time", keep="last").set_index("time") - df1["tide"], df1["surge"], coefs, years = yearly_tide_analysis(df1.anomaly, 365, OPTS) - plot_multiyear_tide_analysis(ASTRO_PLOT, coefs, years, lat, lon, df1, title, filenameOut) - plot_multiyear_tide_analysis(ASTRO_PLOT, coefs, years, lat, lon, df1, title, filenameOut, zoom=True) - # save the coefs calculated and average them - const, mean_amps,mean_phases = demean_amps_phases(coefs, coefs[0]['name']) - js_out = json_format(coefs[-1]) - js_out['weights'] = 0 # weights list is too long and unused in the reconstruction - js_out['A'] = mean_amps.tolist() - js_out['g'] = mean_phases.tolist() - # js_out = js_out.pop('weights') - for key in js_1.keys(): - js_out[key] = js_1[key] - with open(f"tests/data/processed/{IOC_CODE}.json", "w") as fp: - json.dump(js_out, fp) - # fmt: on - - # <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<< - # ~~~~ Jenkins' success message ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - print("\n\nMy work is done\n\n") - sys.exit(0) - - -if __name__ == "__main__": - main() diff --git a/scripts/detide_ioc_all.py b/scripts/detide_ioc_all.py deleted file mode 100644 index 8f491a2..0000000 --- a/scripts/detide_ioc_all.py +++ /dev/null @@ -1,251 +0,0 @@ -#!/usr/bin/env python -import json -import os -import sys - -import numpy as np -import pandas as pd -from searvey import ioc - -from analysea.filters import remove_numerical -from analysea.filters import signaltonoise -from analysea.plot import plot_gaps -from analysea.plot import plot_multiyear_tide_analysis -from analysea.spikes import despike_prominence -from analysea.steps import step_function_ruptures -from analysea.tide import demean_amps_phases -from analysea.tide import yearly_tide_analysis -from analysea.utils import completeness -from analysea.utils import correct_unit -from analysea.utils import detect_gaps -from analysea.utils import json_format - -# =================== -# global variables -# =================== -# load in data information -IOC_STATIONS = ioc.get_ioc_stations() -IOC_STATIONS["lon"] = IOC_STATIONS["geometry"].x -IOC_STATIONS["lat"] = IOC_STATIONS["geometry"].y -IOC_STATIONS = IOC_STATIONS.drop("geometry", axis=1) -# -DATA_FOLDER = "/home/tomsail/Documents/scripts/Python/analysea/data" # put all the stations data in the same folder as this script -OUT = "tests/data" -OUT_JSON = "tests/data/processed" -OUT_FIG = "tests/data/graphs" - -# tidal analysis options -OPTS = { - "conf_int": "linear", - "constit": "auto", - "method": "ols", # ols is faster and good for missing data (Ponchaut et al., 2001) - "order_constit": "frequency", - "Rayleigh_min": 0.97, - "lat": None, - "verbose": False, -} # careful if there is only one Nan parameter, the analysis crashes - -ASTRO_PLOT = [ - "M2", - "S2", - "N2", - "O1", - "K2", - "K1", - "NU2", - "Q1", - "L2", - "P1", - "2N2", - "M4", - "MS4", - "MM", - "MU2", - "SSA", - "LDA2", - "MF", - "MSM", - "MN4", -] - -ASTRO_WRITE = [ - "M2", - "S2", - "N2", - "O1", - "K2", - "K1", - "NU2", - "Q1", - "L2", - "P1", - "2N2", - "M4", - "MS4", - "MM", - "MU2", - "SSA", - "LDA2", - "MF", - "MSM", - "MN4", -] - -IOC_OUT = pd.DataFrame(IOC_STATIONS) - - -# main call -def main(): - for _ioc_file in sorted(os.listdir(DATA_FOLDER)): - if _ioc_file.endswith(".gzip"): - print(" > _ioc_file = ", _ioc_file) - # here is the IOC case - _ioc_code = os.path.splitext(_ioc_file)[0] - _ioc_index = np.where(IOC_OUT.ioc_code == _ioc_code)[0][0] - df0 = pd.read_parquet(os.path.join(DATA_FOLDER, _ioc_code + ".gzip")) - # fill already some info - lat = IOC_OUT.iloc[_ioc_index].lat - lon = IOC_OUT.iloc[_ioc_index].lon - title = IOC_OUT.iloc[_ioc_index].location - filenameOut = os.path.join("./tests/data/graphs", _ioc_code + ".png") - filenameOutGaps = os.path.join("./tests/data/graphs", _ioc_code + "_gaps.png") - OPTS["lat"] = lat - # start completing the json export file - js_1 = dict() - js_1["lat"] = lat - js_1["lon"] = lon - min_time = pd.Timestamp(df0.index.min()) - max_time = pd.Timestamp(df0.index.max()) - js_1["first_obs"] = min_time - js_1["last_obs"] = max_time - - # check if the file exists - if os.path.exists(filenameOut): - print(" > file ", filenameOut, " already exists") - continue - # - try: - df = pd.DataFrame() - df["slevel"] = remove_numerical(df0.slevel) - df["correct"], units_flag = correct_unit(df.slevel) - df["step"], stepsx, steps = step_function_ruptures(df.correct) - threshold = np.max([1, df.correct.std() * 3]) - if (len(stepsx) > 2) and (np.max(steps) > 1.0): # if step are not bigger than 1 meter - ipeaks, peaks, df["correct"] = despike_prominence( - df.correct - df.step, threshold - ) # despike once already - _, _, df["anomaly"] = despike_prominence(df.correct, threshold) - # calculate completeness - cpltness = completeness(df.anomaly) - missing = 100 - np.round(cpltness, 2) - if cpltness < 70: - IOC_OUT.loc[_ioc_index, ["analysis"]] = "Failed: < 70% completeness" - IOC_OUT.loc[_ioc_index, ["missing"]] = missing - IOC_OUT.loc[_ioc_index, ["analysed"]] = False - # - js_1["analysis"] = "Failed: missing data" - js_1["missing"] = missing - js_1["analysed"] = False - # - print("completeness < 70% :", cpltness) - with open(f"./tests/data/processed/{_ioc_code}.json", "w") as f: - json.dump(json_format(js_1), f, indent=2) - continue - # - if (max_time - min_time).days < 365: - IOC_OUT.loc[_ioc_index, ["analysis"]] = "Failed: not enough data" - IOC_OUT.loc[_ioc_index, ["missing"]] = missing - IOC_OUT.loc[_ioc_index, ["analysed"]] = False - # - js_1["analysis"] = "Failed: missing data" - js_1["missing"] = missing - js_1["analysed"] = False - # - print( - "ignore : period is less than a year:", - (max_time - min_time).days, - "days", - ) - with open(f"./tests/data/processed/{_ioc_code}.json", "w") as f: - json.dump(json_format(js_1), f, indent=2) - continue - else: - # detect big gaps - gaps, small_gaps, big_gaps = detect_gaps(df) - plot_gaps(df.anomaly, big_gaps, filenameOutGaps) - # assign parameters before running tides (eventual crash) - IOC_OUT.loc[_ioc_index, ["missing"]] = 100 - np.round(cpltness, 2) - IOC_OUT.loc[_ioc_index, ["biggest_gap"]] = str(big_gaps.max()) - IOC_OUT.loc[_ioc_index, ["total_gaps"]] = len(gaps) - IOC_OUT.loc[_ioc_index, ["steps"]] = len(stepsx) - IOC_OUT.loc[_ioc_index, ["snr"]] = (signaltonoise(df.slevel),) - IOC_OUT.loc[_ioc_index, ["unit_flag"]] = units_flag - # - js_1["missing"] = 100 - np.round(cpltness, 2) - js_1["biggest_gap"] = str(big_gaps.max()) - js_1["total_gaps"] = len(gaps) - js_1["steps"] = len(stepsx) - js_1["snr"] = (signaltonoise(df.slevel),) - js_1["unit_flag"] = units_flag - js_1 = json_format(js_1) - # - df1 = df.reset_index().drop_duplicates(subset="time", keep="last").set_index("time") - df1["tide"], df1["surge"], coefs, years = yearly_tide_analysis(df1.anomaly, 365, OPTS) - # save figures - if len(coefs) == 0: - IOC_OUT.iloc[_ioc_index]["analysis"] = js_1["analysis"] = "Failed: during tides" - with open(f"./tests/data/processed/{_ioc_code}.json", "w") as f: - json.dump(js_1, f, indent=2) - continue - # printing out results - IOC_OUT.loc[_ioc_index, ["perc_analysed"]] = js_1["perc_analysed"] = ( - len(df1.surge) / len(df.slevel) * 100 - ) - IOC_OUT.loc[_ioc_index, ["analysis"]] = js_1["analysis"] = "Success" - IOC_OUT.loc[_ioc_index, ["analysed"]] = js_1["analysed"] = True - # save the coefs calculated and average them - const, mean_amps, mean_phases = demean_amps_phases(coefs, coefs[0]["name"]) - # plots - plot_multiyear_tide_analysis( - ASTRO_PLOT, coefs, years, lat, lon, df1, title, filenameOut - ) - plot_multiyear_tide_analysis( - ASTRO_PLOT, - coefs, - years, - lat, - lon, - df1, - title, - filenameOut, - zoom=True, - ) - js_out = json_format(coefs[-1]) - js_out["weights"] = 0 # weights list is too long and unused in the reconstruction - js_out["A"] = mean_amps.tolist() - js_out["g"] = mean_phases.tolist() - for key in js_1.keys(): - js_out[key] = js_1[key] - # export JSON file - with open(os.path.join(OUT_JSON, f"{_ioc_code}.json"), "w") as fp: - json.dump(js_out, fp) - # export surge - out = pd.DataFrame(data=df1.surge, index=df1.index) - out.to_parquet(os.path.join(OUT, f"{_ioc_code}_surge.gzip"), compression="gzip") - except Exception as e: - print("Error: ", e) - IOC_OUT.loc[_ioc_index, ["first_obs"]] = min_time.strftime("%Y-%m-%dT%H:%M:%S") - IOC_OUT.loc[_ioc_index, ["last_obs"]] = (max_time.strftime("%Y-%m-%dT%H:%M:%S"),) - IOC_OUT.loc[_ioc_index, ["analysed"]] = False - IOC_OUT.to_parquet("IOC_api_analysed.gzip") - with open(f"./data/processed/{_ioc_code}.json", "w") as fp: - json.dump(json_format(js_1), fp, indent=2) - - # <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<< - # ~~~~ Jenkins' success message ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - print("\n\nMy work is done\n\n") - sys.exit(0) - - -if __name__ == "__main__": - main() diff --git a/tests/data/abas.csv b/tests/data/abas.csv new file mode 100644 index 0000000..4c0d96f --- /dev/null +++ b/tests/data/abas.csv @@ -0,0 +1,42881 @@ +time,rad +2023-10-08 00:00:00, +2023-10-08 00:01:00,-0.30759819453768156 +2023-10-08 00:02:00,-0.33509819453768164 +2023-10-08 00:03:00,-0.34119819453768163 +2023-10-08 00:04:00,-0.32289819453768165 +2023-10-08 00:05:00,-0.3807981945376815 +2023-10-08 00:06:00,-0.27409819453768147 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+from analysea.tide import tide_analysis from analysea.utils import cleanup +from analysea.utils import interpolate +from analysea.utils import resample +OPTS = { # tidal analysis options + "constit": "auto", + "method": "ols", # ols is faster and good for missing data (Ponchaut et al., 2001) + "order_constit": "frequency", + "Rayleigh_min": 0.97, + "lat": 16, + "verbose": True, +} # careful if there is only one Nan parameter, the analysis crashes -def detide_test(): - start = pd.Timestamp("2020-01-01") - end = pd.Timestamp("2023-12-31") - # - raw = observer.scrape_ioc_station(ioc_code="acap2", start_date=start, end_date=end) +STATIONS = pytest.mark.parametrize( + "station", + [ + pytest.param("tests/data/abas.csv", id="abas"), + pytest.param("tests/data/abed.csv", id="abed"), + pytest.param("tests/data/abur.csv", id="abur"), + pytest.param("tests/data/acap2.csv", id="acap2"), + pytest.param("tests/data/acnj.csv", id="acnj"), + ], +) + + +@STATIONS +def test_detide(station): + raw = pd.read_csv(station, index_col=0, parse_dates=True) clean = cleanup(raw) + respl = resample(raw) + interp = interpolate(respl) + for sensor in clean.columns: + signal = interp[sensor] + detided = detide(signal, **OPTS) # acapulco latitude + detided.describe() + assert len(detided) > 0 + + +@STATIONS +def test_tide_coef(station): + df = pd.read_csv(station, index_col=0, parse_dates=True) + clean = cleanup(df) + for sensor in clean.columns: + ts = clean[sensor] + tide, surge, coefs = tide_analysis(ts, **OPTS) + + assert isinstance(tide, pd.Series) + assert not df.empty + - detided = detide(clean, lat=16, verbose=False) # acapulco latitude - detided.describe() +test_tide_coef("tests/data/abas.csv") diff --git a/tests/yearly_tide_coef_test.py b/tests/yearly_tide_coef_test.py deleted file mode 100644 index b531d0a..0000000 --- a/tests/yearly_tide_coef_test.py +++ /dev/null @@ -1,23 +0,0 @@ -import observer -import pandas as pd - -from analysea.tide import yearly_tide_analysis -from analysea.utils import cleanup - - -def yearly_tide_test(): - start = pd.Timestamp("2020-01-01") - end = pd.Timestamp("2023-12-31") - # - raw = observer.scrape_ioc( - ioc_codes=[ - "acap2", - "abed", - ], - start_date=start, - end_date=end, - ) - - clean = cleanup(raw) - - tide, surge, coefs, years = yearly_tide_analysis(clean, lat=16, verbose=False) # acapulco latitude